178 Dask Jobs

We are seeking an experienced Core Python Developer for an immediate on-site opportunity in Hyderabad. This role involves working with cutting-edge technologies in AI/ML, cloud platforms, and Python-based frameworks, delivering scalable solutions for our client. About the Role This role involves working with cutting-edge technologies in AI/ML, cloud platforms, and Python-based frameworks, delivering scalable solutions for our client. Responsibilities Develop and maintain robust applications using Python frameworks (Django, Flask, or Pyramid). Design and implement data pipelines and workflows utilizing Numpy, Scipy, Pandas, Dask, and other advanced libraries. Work with NLP libraries (spaCy, NLTK) and machine learning frameworks (scikit-learn, PyTorch). Develop, test, and maintain RESTful APIs. Collaborate in an Agile development environment, following best coding and architectural practices. Integrate with SQL/NoSQL databases for optimal data management. Implement solutions on cloud platforms such as AWS, Google Cloud, or Azure. Use version control and collaborative workflows with Git. Build and optimize AI/ML pipelines, potentially using Langchain or similar tools. Required Skills Core Python Development, AI/ML technologies, Cloud platforms, Python frameworks, RESTful APIs, Agile development, SQL/NoSQL databases, Version control with Git. Preferred Skills Experience with NLP libraries, machine learning frameworks, and building AI/ML pipelines.

As an AI/ML Developer, you will be an integral part of our team of researchers, data scientists, and developers. Your primary responsibility will be to work on cutting-edge AI solutions in various industries such as commerce, agriculture, insurance, financial markets, and procurement. This will involve developing and optimizing machine learning and generative AI models to address real-world challenges effectively. Your key responsibilities will include developing and optimizing ML, NLP, Deep Learning, and Generative AI models. You will be expected to research and implement state-of-the-art algorithms for both supervised and unsupervised learning. Working with large-scale datasets in distributed environments will be a crucial aspect of your role. It is essential to have a deep understanding of business processes to select and apply the most suitable ML approaches and ensure the scalability and performance of ML solutions. Collaboration with cross-functional teams, including product owners, designers, and developers, will be essential for the success of projects. You will be required to solve complex data integration and deployment challenges and effectively communicate results using data visualization techniques. As part of a global team, you will collaborate across different time zones. The ideal candidate will have strong experience in Machine Learning, Deep Learning, NLP, and Generative AI. Hands-on expertise in frameworks like TensorFlow, PyTorch, or Hugging Face Transformers is crucial. Experience with Large Language Models (LLMs), model fine-tuning, and prompt engineering is highly desirable. Proficiency in Python, R, or Scala for ML development is required, along with knowledge of cloud-based ML platforms such as AWS, Azure, or GCP. Experience with big data processing tools like Spark, Hadoop, or Dask is a plus. The ability to scale ML models from prototypes to production, coupled with strong analytical and problem-solving skills, will be key to excelling in this role. If you are passionate about pushing the boundaries of ML and Generative AI, we are excited to hear from you!,

Job Description This is a unique opportunity to apply your skills and contribute to impactful global business initiatives. As an Applied AI ML Lead - Data Scientist- Vice President at JPMorgan Chase within the Commercial & Investment Bank's Global Banking team, you’ll leverage your technical expertise and leadership abilities to support AI innovation. You should have deep knowledge of AI/ML and effective leadership to inspire the team, align cross-functional stakeholders, engage senior leadership, and drive business results. Job Responsibilities Lead a local AI/ML team with accountability and engagement into a global organization. Mentor and guide team members, fostering an inclusive culture with a growth mindset. Collaborate on setting the technical vision and executing strategic roadmaps to drive AI innovation. Deliver AI/ML projects through our ML development life cycle using Agile methodology. Help transform business requirements into AI/ML specifications, define milestones, and ensure timely delivery. Work with product and business teams to define goals and roadmaps. Maintain alignment with cross-functional stakeholders. Exercise sound technical judgment, anticipate bottlenecks, escalate effectively, and balance business needs versus technical constraints. Design experiments, establish mathematical intuitions, implement algorithms, execute test cases, validate results and productionize highly performant, scalable, trustworthy and often explainable solution. Mentor Junior Machine Learning associates in delivering successful projects and building successful career in the firm. Participate and contribute back to firmwide Machine Learning communities through patenting, publications and speaking engagements. Evaluate and design effective processes and systems to facilitate communication, improve execution, and ensure accountability. Required Qualifications, Capabilities, And Skills 14+ years (BS) or 8+ (MS) or 5+ (PhD) years of relevant in Computer Science, Data Science, Information Systems, Statistics, Mathematics or equivalent experience. Track record of managing AI/ML or software development teams. Experience as a hands-on practitioner developing production AI/ML solutions. Deep knowledge and experience in machine learning and artificial intelligence. Ability to set teams up for success in speed and quality, and design effective metrics and hypotheses. Expert in at least one of the following areas: Natural Language Processing, Knowledge Graph, Computer Vision, Speech Recognition, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis. Deep knowledge in Data structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, Statistics. Demonstrated expertise in machine learning frameworks: Tensorflow, Pytorch, pyG, Keras, MXNet, Scikit-Learn. Strong programming knowledge of python, spark; Strong grasp on vector operations using numpy, scipy; Strong grasp on distributed computation using Multithreading, Multi GPUs, Dask, Ray, Polars etc. Familiarity in AWS Cloud services such as EMR, Sagemaker etc., Strong people management and team-building skills. Ability to coach and grow talent, foster a healthy engineering culture, and attract/retain talent. Ability to build a diverse, inclusive, and high-performing team. Ability to inspire collaboration among teams composed of both technical and non-technical members. Effective communication, solid negotiation skills, and strong leadership. About Us JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management. We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation. About The Team J.P. Morgan’s Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. Job Description: Manager ML Ops Role Overview: We are seeking an accomplished and visionary Manager Data Scientist with minimum 8 Years of experience in Data Science and Machine learning, preferable experience around NLP, Generative AI, LLMs, MLOps, Optimization techniques and AI solution Architecture to lead our AI team and drive the strategic direction of AI initiatives. In this role you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise and leadership skills. The ideal candidate should have a proven track record in AI leadership, a deep understanding of AI technologies, and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role. Minimum 8 Years of experience in Data Science and Machine learning. Excellent leadership skills with at least 2-3 years of people management OR technical architecture experience. Responsibilities: ML Ops Key Responsibilities Develop, deploy, and monitor machine learning models in production environments. Automate ML pipelines for model training, validation, and deployment . Optimize ML model performance, scalability, and cost efficiency. Implement CI/CD workflows for ML model versioning, testing, and deployment. Manage and optimize data processing workflows for structured and unstructured data. Design, build, and maintain scalable ML infrastructure on cloud platforms. Implement monitoring, logging, and alerting solutions for model performance tracking . Collaborate with data scientists, software engineers, and DevOps teams to integrate ML models into business applications. Ensure compliance with best practices for security, data privacy, and governance . Stay updated with the latest trends in MLOps, AI, and cloud technologies . Mandatory Skills Technical Skills: Programming Languages : Proficiency in Python (3.x) and SQL . ML Frameworks & Libraries : Extensive knowledge of ML frameworks ( TensorFlow, PyTorch, Scikit-learn ), data structures, data modeling, and software architecture. Databases : Experience with SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra, DynamoDB) databases. Mathematics & Algorithms : Strong understanding of mathematics, statistics, and algorithms for machine learning applications. ML Modules & REST API : Experience in developing and integrating ML modules with RESTful APIs . Version Control : Hands-on experience with Git and best practices for version control. Model Deployment & Monitoring : Experience in deploying and monitoring ML models using: MLflow (for model tracking, versioning, and deployment) WhyLabs (for model monitoring and data drift detection) Kubeflow (for orchestrating ML workflows) Airflow (for managing ML pipelines) Docker & Kubernetes (for containerization and orchestration) Prometheus & Grafana (for logging and real-time monitoring) Data Processing : Ability to process and transform unstructured data into meaningful insights (e.g., auto-tagging images, text-to-speech conversions ). Preferred Cloud & Infrastructure Skills: Experience with cloud platforms : Knowledge of AWS Lambda, AWS API Gateway, AWS Glue, Athena, S3 and Iceberg and Azure AI Studio for model hosting, GPU/TPU usage, and scalable infrastructure. Hands-on with Infrastructure as Code (Terraform, CloudFormation) for cloud automation. Experience on CI/CD pipelines: Experience integrating ML models into continuous integration/continuous delivery workflows. We use Git based CI/CD methods mostly. Experience with feature stores (Feast, Tecton) for managing ML features. Knowledge of big data processing tools (Spark, Hadoop, Dask, Apache Beam) . EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. Key Responsibilities Develop, deploy, and monitor machine learning models in production environments. Automate ML pipelines for model training, validation, and deployment. Optimize ML model performance, scalability, and cost efficiency. Implement CI/CD workflows for ML model versioning, testing, and deployment. Manage and optimize data processing workflows for structured and unstructured data. Design, build, and maintain scalable ML infrastructure on cloud platforms. Implement monitoring, logging, and alerting solutions for model performance tracking. Collaborate with data scientists, software engineers, and DevOps teams to integrate ML models into business applications. Ensure compliance with best practices for security, data privacy, and governance. Stay updated with the latest trends in MLOps, AI, and cloud technologies. Mandatory Skills Technical Skills: Programming Languages: Proficiency in Python (3.x) and SQL. ML Frameworks & Libraries: Extensive knowledge of ML frameworks (TensorFlow, PyTorch, Scikit-learn), data structures, data modeling, and software architecture. Databases: Experience with SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra, DynamoDB) databases. Mathematics & Algorithms: Strong understanding of mathematics, statistics, and algorithms for machine learning applications. ML Modules & REST API: Experience in developing and integrating ML modules with RESTful APIs. Version Control: Hands-on experience with Git and best practices for version control. Model Deployment & Monitoring: Experience in deploying and monitoring ML models using:MLflow (for model tracking, versioning, and deployment) WhyLabs (for model monitoring and data drift detection) Kubeflow (for orchestrating ML workflows) Airflow (for managing ML pipelines) Docker & Kubernetes (for containerization and orchestration) Prometheus & Grafana (for logging and real-time monitoring) Data Processing: Ability to process and transform unstructured data into meaningful insights (e.g., auto-tagging images, text-to-speech conversions). Preferred Cloud & Infrastructure Skills: Experience with cloud platforms : Knowledge of AWS Lambda, AWS API Gateway, AWS Glue, Athena, S3 and Iceberg and Azure AI Studio for model hosting, GPU/TPU usage, and scalable infrastructure. Hands-on with Infrastructure as Code (Terraform, CloudFormation) for cloud automation. Experience on CI/CD pipelines: Experience integrating ML models into continuous integration/continuous delivery workflows. We use Git based CI/CD methods mostly. Experience with feature stores (Feast, Tecton) for managing ML features. Knowledge of big data processing tools (Spark, Hadoop, Dask, Apache Beam). EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

As a Machine Learning Engineer, you will be responsible for designing and implementing scalable machine learning models throughout the entire lifecycle - from data preprocessing to deployment. Your role will involve leading feature engineering and model optimization efforts to enhance performance and accuracy. Additionally, you will build and manage end-to-end ML pipelines using MLOps practices, ensuring seamless deployment, monitoring, and maintenance of models in production environments. Collaboration with data scientists and product teams will be key in understanding business requirements and translating them into effective ML solutions. You will conduct advanced data analysis, create visualization dashboards for insights, and maintain detailed documentation of models, experiments, and workflows. Moreover, mentoring junior team members on best practices and technical skills will be part of your responsibilities to foster growth within the team. In terms of required skills, you must have at least 3 years of experience in machine learning development, with a focus on the end-to-end model lifecycle. Proficiency in Python using Pandas, NumPy, and Scikit-learn for advanced data handling and feature engineering is crucial. Strong hands-on expertise in TensorFlow or PyTorch for deep learning model development is also a must-have. Desirable skills include experience with MLOps tools like MLflow or Kubeflow for model management and deployment, familiarity with big data frameworks such as Spark or Dask, and exposure to cloud ML services like AWS SageMaker or GCP AI Platform. Additionally, working knowledge of Weights & Biases and DVC for experiment tracking and versioning, as well as experience with Ray or BentoML for distributed training and model serving, will be considered advantageous. Join our team and contribute to cutting-edge machine learning projects while continuously improving your skills and expertise in a collaborative and innovative environment.,

TECHNICAL ARCHITECT Key Responsibilities 1. Designing technology systems: Plan and design the structure of technology solutions, and work with design and development teams to assist with the process. 2. Communicating: Communicate system requirements to software development teams, and explain plans to developers and designers. They also communicate the value of a solution to stakeholders and clients. 3. Managing Stakeholders: Work with clients and stakeholders to understand their vision for the systems. Should also manage stakeholder expectations. 4. Architectural Oversight: Develop and implement robust architectures for AI/ML and data science solutions, ensuring scalability, security, and performance. Oversee architecture for data-driven web applications and data science projects, providing guidance on best practices in data processing, model deployment, and end-to-end workflows. 5. Problem Solving: Identify and troubleshoot technical problems in existing or new systems. Assist with solving technical problems when they arise. 6. Ensuring Quality: Ensure if systems meet security and quality standards. Monitor systems to ensure they meet both user needs and business goals. 7. Project management: Break down project requirements into manageable pieces of work, and organise the workloads of technical teams. 8. Tool & Framework Expertise: Utilise relevant tools and technologies, including but not limited to LLMs, TensorFlow, PyTorch, Apache Spark, cloud platforms (AWS, Azure, GCP), Web App development frameworks and DevOps practices. 9. Continuous Improvement: Stay current on emerging technologies and methods in AI, ML, data science, and web applications, bringing insights back to the team to foster continuous improvement. Technical Skills 1. Proficiency in AI/ML frameworks such as TensorFlow, PyTorch, Keras, and scikit-learn for developing machine learning and deep learning models. 2. Knowledge or experience working with self-hosted or managed LLMs. 3. Knowledge or experience with NLP tools and libraries (e.g., SpaCy, NLTK, Hugging Face Transformers) and familiarity with Computer Vision frameworks like OpenCV and related libraries for image processing and object recognition. 4. Experience or knowledge in back-end frameworks (e.g., Django, Spring Boot, Node.js, Express etc.) and building RESTful and GraphQL APIs. 5. Familiarity with microservices, serverless, and event-driven architectures. Strong understanding of design patterns (e.g., Factory, Singleton, Observer) to ensure code scalability and reusability. 6. Proficiency in modern front-end frameworks such as React, Angular, or Vue.js, with an understanding of responsive design, UX/UI principles, and state management (e.g., Redux) 7. In-depth knowledge of SQL and NoSQL databases (e.g., PostgreSQL, MongoDB, Cassandra), as well as caching solutions (e.g., Redis, Memcached). 8. Expertise in tools such as Apache Spark, Hadoop, Pandas, and Dask for large-scale data processing. 9. Understanding of data warehouses and ETL tools (e.g., Snowflake, BigQuery, Redshift, Airflow) to manage large datasets. 10. Familiarity with visualisation tools (e.g., Tableau, Power BI, Plotly) for building dashboards and conveying insights. 11. Knowledge of deploying models with TensorFlow Serving, Flask, FastAPI, or cloud-native services (e.g., AWS SageMaker, Google AI Platform). 12. Familiarity with MLOps tools and practices for versioning, monitoring, and scaling models (e.g., MLflow, Kubeflow, TFX). 13. Knowledge or experience in CI/CD, IaC and Cloud Native toolchains. 14. Understanding of security principles, including firewalls, VPC, IAM, and TLS/SSL for secure communication. 15. Knowledge of API Gateway, service mesh (e.g., Istio), and NGINX for API security, rate limiting, and traffic management. Experience Required Technical Architect with 7 - 12 years of experience Salary 22-25 LPA Job Types: Full-time, Permanent Pay: ₹2,200,000.00 - ₹2,500,000.00 per year Work Location: In person

Step into the world of AI innovation with the Experts Community of Soul AI (By Deccan AI). We are looking for India’s top 1% Data Scientists for a unique job opportunity to work with the industry leaders. Who can be a part of the community? We are looking for top-tier Data Scientists with expertise in predictive modeling, statistical analysis, and A/B testing. If you have experience in this field then this is your chance to collaborate with industry leaders. What’s in it for you? Pay above market standards The role is going to be contract based with project timelines from 2 - 12 months , or freelancing. Be a part of an Elite Community of professionals who can solve complex AI challenges. Work location could be: Remote (Highly likely) Onsite on client location Deccan AI’s Office: Hyderabad or Bangalore Responsibilities: Lead design, development, and deployment of scalable data science solutions optimizing large-scale data pipelines in collaboration with engineering teams. Architect advanced machine learning models (deep learning, RL, ensemble) and apply statistical analysis for business insights. Apply statistical analysis, predictive modeling, and optimization techniques to derive actionable business insights. Own the full lifecycle of data science projects—from data acquisition, preprocessing, and exploratory data analysis (EDA) to model development, deployment, and monitoring. Implement MLOps workflows (model training, deployment, versioning, monitoring) and conduct A/B testing to validate models. Required Skills: Expert in Python, data science libraries (Pandas, NumPy, Scikit-learn), and R with extensive experience with machine learning (XGBoost, PyTorch, TensorFlow) and statistical modeling. Proficient in building scalable data pipelines (Apache Spark, Dask) and cloud platforms (AWS, GCP, Azure). Expertise in MLOps (Docker, Kubernetes, MLflow, CI/CD) along with strong data visualization skills (Tableau, Plotly Dash) and business acumen. Nice to Have: Experience with NLP, computer vision, recommendation systems, or real-time data processing (Kafka, Flink). Knowledge of data privacy regulations (GDPR, CCPA) and ethical AI practices. Contributions to open-source projects or published research. What are the next steps? 1. Register on our Soul AI website. 2. Our team will review your profile. 3. Clear all the screening rounds: Clear the assessments once you are shortlisted. As soon as you qualify all the screening rounds (assessments, interviews) you will be added to our Expert Community! 4. Profile matching and Project Allocation: Be patient while we align your skills and preferences with the available project. Skip the Noise. Focus on Opportunities Built for You!

Summary As a Data Scientist you will build and deploy data-driven solutions to support business goals. You will use your skills in data analytics, machine learning (supervised and unsupervised) and GenAI, to translate complex data into actionable insights. As a data scientist you will work closely with cross-functional team of data engineers, product owners, Devops and bridge the gap between technical implementation and business needs. Responsibilities (Other duties may be assigned.) Experiment and feature engineer with data to design and build machine/deep learning models with appropriate precision, recall, F1 scores to meet the use case need Prompt engineer to develop new and enhance existing Gen-AI applications. (Chatbots, RAG). Develop and implement advanced AI agents capable of performing autonomous tasks, decision-making, and executing requirement-specific workflows. Document and create experiment reports on the implementation and code in a way that is clear and accessible to both technical and non-technical team members. Perform advanced data analysis, manipulation, and cleansing to extract actionable insights from structured and unstructured data. Create scalable and efficient recommendation systems that enhance user personalization and engagement. Effectively communicate technical solutions and findings to both technical and non-technical stakeholders. Design and deploy AI-driven chatbots and virtual assistants, focusing on natural language understanding and contextual relevance. Implement and optimize supervised and unsupervised learning models for NLP tasks, including text classification, sentiment analysis, and language generation. Explore, understand, and develop state-of-the-art technologies for AI agents, integrating them with broader enterprise systems. Collaborate with cross-functional teams to gather business requirements and deliver AI-driven solutions tailored to specific use cases. Automate workflows using advanced AI tools and frameworks to increase efficiency and reduce manual interventions. Stay informed about cutting-edge advancements in AI, machine learning, NLP, and Gen AI applications, and assess their relevance to the organization. Education and/or experience: At least 5 years of experience working with data sciences. Preferably with a bachelors (OR Master) degree in Computer Science, Data Science, or Artificial Intelligence. Knowledge Skills and Abilities: Strong understanding of mathematics including vector algebra and probability theory for understanding and explaining machine learning (discriminative and generative) models. Strong expertise in data analytics, pattern recognition, machine learning, including predictive modeling and recommendation systems. Excellent communication & documentation skills to articulate complex ideas to diverse audiences. Hands-on experience with large datasets and using distributed systems for analytics and modelling. Advanced understanding of natural language processing (NLP) techniques and tools, including transformers like BERT, GPT, or similar models including open-source LLMs. Strong knowledge of cloud platforms (AWS) for deploying and scaling AI models. Proficiency with code versioning platforms like CodeCommit and GitHub. Technical Skills: Python proficiency and hands-on experience with libraries like (Pandas, Dask, Numpy, Matplotlib, NLTK, Sklearn, Pytorch and Tensorflow). Experience in prompt engineering for AI models to enhance functionality and adaptability. Familiarity with AI agent frameworks like LangChain, OpenAI APIs, or other agent-building tools. Advanced skills in Relational databases [Postgres], Vector Database, querying, analytics, semantic search, and data manipulation. Strong problem-solving and critical-thinking skills, with the ability to handle complex technical challenges. Hands-on experience working with API frameworks like Flask, FastAPI, etc. Proficiency with code versioning platforms like CodeCommit and GitHub. Preferred: Hands-on experience building and deploying conversational AI, chatbots, and virtual assistants. Familiarity with MLOps pipelines and CI/CD for AI/ML workflows. Experience with reinforcement learning or multi-agent systems. Language Skills Ability to speak the English language proficiently, both verbally and in writing. Work Environment The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Employee works primarily in a home office environment. The home office must be a well-defined work area, separate from normal domestic activity and complete with all essential technology including, but not limited to; separate phone, scanner, printer, computer, etc. as required in order to effectively perform their duties. Compliance with all relevant FINEOS Global policies and procedures related to Quality, Security, Safety, Business Continuity, and Environmental systems. Travel and fieldwork, including international travel may be required. Therefore, employee must possess, or be able to acquire a valid passport. Must be legally eligible to work in the country in which you are hired. FINEOS is an Equal Opportunity Employer. FINEOS does not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, non-disqualifying physical or mental disability, national origin, veteran status or any other basis covered by appropriate law. All employment is decided on the basis of qualifications, merit, and business need.

Join us at Provectus to be a part of a team that is dedicated to building cutting-edge technology solutions that have a positive impact on society. Our company specializes in AI and ML technologies, cloud services, and data engineering, and we take pride in our ability to innovate and push the boundaries of what's possible. As an ML Engineer, you’ll be provided with all opportunities for development and growth. Let's work together to build a better future for everyone! Requirements: Comfortable with standard ML algorithms and underlying math. Strong hands-on experience with LLMs in production, RAG architecture, and agentic systems AWS Bedrock experience strongly preferred Practical experience with solving classification and regression tasks in general, feature engineering. Practical experience with ML models in production. Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines. Solid software engineering skills (i.e., ability to produce well-structured modules, not only notebook scripts). Python expertise, Docker. English level - strong Intermediate. Excellent communication and problem-solving skills. Will be a plus: Practical experience with cloud platforms (AWS stack is preferred, e.g. Amazon SageMaker, ECR, EMR, S3, AWS Lambda). Practical experience with deep learning models. Experience with taxonomies or ontologies. Practical experience with machine learning pipelines to orchestrate complicated workflows. Practical experience with Spark/Dask, Great Expectations. Responsibilities: Create ML models from scratch or improve existing models. Collaborate with the engineering team, data scientists, and product managers on production models. Develop experimentation roadmap. Set up a reproducible experimentation environment and maintain experimentation pipelines. Monitor and maintain ML models in production to ensure optimal performance. Write clear and comprehensive documentation for ML models, processes, and pipelines. Stay updated with the latest developments in ML and AI and propose innovative solutions.

You will be part of a dynamic team of researchers, data scientists, and developers as an AI/ML Developer. Your primary responsibility will involve working on cutting-edge AI solutions in various industries like commerce, agriculture, insurance, financial markets, and procurement. Your focus will be on developing and optimizing machine learning and generative AI models to address real-world challenges effectively. Your key responsibilities will include developing and optimizing ML, NLP, Deep Learning, and Generative AI models. You will be required to research and implement advanced algorithms for supervised and unsupervised learning. Additionally, you will work with large-scale datasets in distributed environments and understand business processes to select and apply the most suitable ML approaches. Ensuring the scalability and performance of ML solutions will also be a crucial part of your role. Collaboration with cross-functional teams, such as product owners, designers, and developers, will be essential. You will be expected to solve intricate data integration and deployment challenges while effectively communicating results using data visualization. Working in global teams across different time zones will also be part of your job scope. To be successful in this role, you should possess strong experience in Machine Learning, Deep Learning, NLP, and Generative AI. Hands-on expertise in frameworks like TensorFlow, PyTorch, or Hugging Face Transformers is required. Experience with LLMs, model fine-tuning, and prompt engineering will be beneficial. Proficiency in Python, R, or Scala for ML development is necessary, along with knowledge of cloud-based ML platforms such as AWS, Azure, and GCP. Experience with big data processing using tools like Spark, Hadoop, or Dask is also preferred. Your ability to scale ML models from prototypes to production and your strong analytical and problem-solving skills will be highly valued. If you are enthusiastic about pushing the boundaries of ML and GenAI, we are excited to hear from you!,

As a Python Developer, you will be responsible for leveraging your experience to build standard supervised (GLM ensemble techniques) and unsupervised (clustering) models using industry libraries such as pandas, sklearn, and keras. Your expertise in big data technologies like Spark and Dask, as well as databases including SQL and NoSQL, will be crucial in this role. Your role will also involve significant experience in Python, encompassing tasks such as writing unit tests, developing packages, and crafting reusable and maintainable code. An essential aspect of your responsibilities will be the ability to comprehend and articulate modeling techniques, along with visualizing analytical results using tools like matplotlib, seaborn, plotly, D3, and tableau. Experience with continuous integration/development tools like Jenkins and Spark ML pipelines will be advantageous. We are looking for a self-starter who not only excels individually but also collaborates effectively with colleagues, bringing innovative ideas to enhance our collective mindset. For the ideal candidate, possessing an advanced degree with a strong foundation in the mathematical principles underpinning machine learning, such as linear algebra and multivariate calculus, would be a significant advantage. Additionally, expertise in specialized areas like reinforcement learning, NLP, Bayesian techniques, or generative models would be highly valued. Your ability to effectively present ideas and analytical findings in a compelling manner that influences stakeholders will be a key aspect of this role. Demonstrated experience in developing analytical solutions within an industry context and a genuine passion for utilizing data science ethically to enhance customer-centricity in financial services will set you apart. If you are ready to contribute to a dynamic team by applying your Python development skills and data science expertise to drive impactful solutions in the financial services sector, we encourage you to explore this opportunity further.,

hackajob is collaborating with J.P. Morgan to connect them with exceptional tech professionals for this role. We have an exciting and rewarding opportunity for you to take your Predictive Science career to the next level. As an Applied AI ML Lead - Data Scientist- Vice President at JPMorgan Chase within the Commercial & Investment Bank's Global Banking team, you’ll leverage your technical expertise and leadership abilities to support AI innovation. You should have deep knowledge of AI/ML and effective leadership to inspire the team, align cross-functional stakeholders, engage senior leadership, and drive business results. Job Responsibilities Lead a local AI/ML team with accountability and engagement into a global organization. Mentor and guide team members, fostering an inclusive culture with a growth mindset. Collaborate on setting the technical vision and executing strategic roadmaps to drive AI innovation. Deliver AI/ML projects through our ML development life cycle using Agile methodology, help transform business requirements into AI/ML specifications, define milestones and ensure timely delivery. Work with product and business teams to define goals and roadmaps, maintain alignment with cross-functional stakeholders. Exercise sound technical judgment, anticipate bottlenecks, escalate effectively, and balance business needs versus technical constraints. Design experiments, establish mathematical intuitions, implement algorithms, execute test cases, validate results and productionize highly performant, scalable, trustworthy and often explainable solution. Mentor junior team members in delivering successful projects and building successful career in the firm. Participate and contribute back to firmwide Machine Learning communities through patenting, publications and speaking engagements. Evaluate and design effective processes and systems to facilitate communication, improve execution and ensure accountability. Required Qualifications, Capabilities, And Skills Formal training or certification on Predictive Science concepts and 5+ years applied experience Track record of managing AI/ML or software development teams and experience as a hands-on practitioner developing production AI/ML solutions. Deep knowledge and experience in machine learning, artificial intelligence and ability to set teams up for success in speed and quality, design effective metrics and hypotheses. Expert in at least one of the following areas: Large Language Models, Natural Language Processing, Knowledge Graph, Computer Vision, Speech Recognition, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis. Good understanding of Data structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, Statistics. Demonstrated expertise in machine learning frameworks: Tensorflow, Pytorch, pyG, Keras, MXNet, Scikit-Learn. Strong programming knowledge of python, spark, strong grasp on vector operations using numpy, scipy and strong grasp on distributed computation using Multithreading, Multi GPUs, Dask, Ray, Polars etc. Familiarity with agentic workflows and relevant frameworks, such as LangChain, LangGraph, Auto-GPT etc. Familiarity in AWS Cloud services such as EMR, Sagemaker etc., Strong people management, team-building skills and ability to coach and grow talent, foster a healthy engineering culture and attract/retain talent, build a diverse, inclusive, and high-performing team. Ability to inspire collaboration among teams composed of both technical and non-technical members, effective communication, solid negotiation skills, and strong leadership. Preferred Qualifications, Capabilities, And Skills 14+ years (BE/BTech/BS) or 8+ (ME/MTech/MS) or 5+ (PhD) years of relevant experience in Computer Science, Information Systems, Statistics, Mathematics, or equivalent experience. Experience working at code level About Us JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management. We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation. About The Team J.P. Morgan’s Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.

Do you want to work on complex and pressing challenges—the kind that bring together curious, ambitious, and determined leaders who strive to become better every day? If this sounds like you, you’ve come to the right place. Your Impact As a Data Engineer at QuantumBlack, you will collaborate with stakeholders, data scientists, and internal teams to develop and implement data products and solutions. Key responsibilities include building and maintaining technical platforms for advanced analytics, designing scalable and reproducible data pipelines for machine learning, and ensuring information security in data environments. You will assess clients' data quality, map data fields to hypotheses, and prepare data for analytics models. Additionally, you will contribute to R&D projects, internal asset development, and cross-functional problem-solving sessions with various stakeholders, including C-level executives, to create impactful analytics solutions. You will be based in Gurugram or Bengaluru as part of a global data engineering community and you will work in cross-functional and Agile project teams alongside project managers, data scientists, machine learning engineers, other data engineers and industry experts. You will work hand in hand with our clients from data owners and users to C-level executives. You will be aligned to one of our practice Pharma & Medical Products (PMP) or Global Energy & Materials (GEM) to work on similar industry clients. Our PMP practice focuses on helping clients bring life-saving medicines and medical treatments to patients. You will work with the Advanced Analytics team across Research & Development (R&D), Operations, and Commercial to build and scale digital and analytical approaches. This practice is one of the fastest growing practices and is comprised of a tight-knit community of consultants, research, solution, data, and practice operations colleagues across the firm. PMP is also one of the most globally connected sector practices, offering ample global exposure. Our GEM practice supports clients in a wide range of industries including chemicals, steel, mining, pulp & paper, electric power and oil & gas, among other on the way to operational excellence. Energy and materials industries are big and important to the world’s economy and you as a key player will face significant challenges such as meeting growing demands, operating productively, and managing gigantic capital investments. McKinsey has an unparalleled reputation in these industries across the world, and today serves most of the top players globally. GEMx and PMPx is the practice’s assetization arm focused on creating reusable digital and analytics assets to support our client work. They work directly with clients to build and scale digital and analytical approaches to addressing their most persistent priorities e.g., PMPx builds and operates tools that support senior executives in pharma and device manufacturers, for whom evidence-based decision-making and competitive intelligence are paramount. As part of our group, you will join a global practice solving problems for large organizations in our GEM practice and building their capabilities for sustained impact. You will work on the frameworks and libraries that our teams of Data Scientists and Data Engineers use to progress from data to impact. You will guide global companies through data science solutions to transform their businesses and enhance performance across industries including healthcare, automotive, energy and elite sport. Real-World Impact: We provide unique learning and development opportunities internationally. Fusing Tech & Leadership: We work with the latest technologies and methodologies and offer first class learning programs at all levels. Multidisciplinary Teamwork: Our teams include data scientists, engineers, project managers, UX and visual designers who work collaboratively to enhance performance. Innovative Work Culture: Creativity, insight and passion come from being balanced. We cultivate a modern work environment through an emphasis on wellness, insightful talks and training sessions. Striving for Diversity: With colleagues from over 40 nationalities, we recognize the benefits of working with people from all walks of life. While we advocate for using the right tech for the right task, we often leverage the following technologies: Python, PySpark, the PyData stack, SQL, Airflow, Databricks, our own open-source data pipelining framework called Kedro, Dask/RAPIDS, container technologies such as Docker and Kubernetes, cloud solutions such as AWS, GCP, and Azure, and more! As a Data Engineer, you will: Collaborate with business stakeholders, data scientists and internal teams to build and implement extraordinary domain focused data products (re-usable asset) and solutions and delivering them right to the client Develop deep domain understanding Use new and creative techniques to deliver impact for our clients as well as R&D projects Help to build and maintain the technical platform for advanced analytics engagements, spanning data science and data engineering work Design and build data pipelines for machine learning that are robust, modular, scalable, deployable, reproducible, and versioned Create and manage data environments and ensure information security standards are maintained at all times Understand clients data landscape and assess data quality Map data fields to hypotheses and curate, wrangle, and prepare data for use in advanced analytics models Have the opportunity to contribute to R&D projects and internal asset development Contribute to cross-functional problem-solving sessions with your team and our clients, from data owners and users to C-level executives, to address their needs and build impactful analytics solutions Your Growth Driving lasting impact and building long-term capabilities with our clients is not easy work. You are the kind of person who thrives in a high performance/high reward culture - doing hard things, picking yourself up when you stumble, and having the resilience to try another way forward. In return for your drive, determination, and curiosity, we'll provide the resources, mentorship, and opportunities you need to become a stronger leader faster than you ever thought possible. Your colleagues—at all levels—will invest deeply in your development, just as much as they invest in delivering exceptional results for clients. Every day, you'll receive apprenticeship, coaching, and exposure that will accelerate your growth in ways you won’t find anywhere else. When you join us, you will have: Continuous learning: Our learning and apprenticeship culture, backed by structured programs, is all about helping you grow while creating an environment where feedback is clear, actionable, and focused on your development. The real magic happens when you take the input from others to heart and embrace the fast-paced learning experience, owning your journey. A voice that matters: From day one, we value your ideas and contributions. You’ll make a tangible impact by offering innovative ideas and practical solutions. We not only encourage diverse perspectives, but they are critical in driving us toward the best possible outcomes. Global community: With colleagues across 65+ countries and over 100 different nationalities, our firm’s diversity fuels creativity and helps us come up with the best solutions for our clients. Plus, you’ll have the opportunity to learn from exceptional colleagues with diverse backgrounds and experiences. World-class benefits: On top of a competitive salary (based on your location, experience, and skills), we provide a comprehensive benefits package to enable holistic well-being for you and your family. Your qualifications and skills Bachelor's degree in computer science or related field; master's degree is a plus 2+ years of relevant work experience Experience with at least one of the following technologies: Python, Scala, Java Strong proven experience on distributed processing frameworks (Spark, Hadoop, EMR) and SQL is very much expected; commercial client- facing project experience is helpful, including working in close-knit teams Ability to work across structured, semi-structured, and unstructured data, extracting information and identifying linkages across disparate data sets Proven ability in clearly communicating complex solutions; strong attention to detail Understanding of information security principles to ensure compliant handling and management of client data Experience and interest in Cloud platforms such as: AWS, Azure, Google Platform or Databricks Good to have experience in CI/CD using GitHub Actions or CircleCI or any other CI/CD tech stack and experience in end to end pipeline development including application deployment

Data Scientist — Quantitative Machine Learning Location Hyderabad (preferred) — hybrid friendly. Exceptional remote candidates within India will be considered. About InvestorAi InvestorAi is an AI-first fintech that transforms vast, noisy market data into real-time, alpha-generating insights for global investors. Our proprietary “spatial-ordering” pipelines, genetic-algorithm search, and deep-learning mixture-of-experts have delivered a 45 % CAGR while remaining lean and profitable. With new capital and an ambitious roadmap, we’re scaling research and production deployment of models that trade live capital every day. Our Al algorithms have been trained and developed by our expert tam at Bridgeweave Labs using 14 years of stock market data. We perform over 35 million computations using sophisticated AI techniques like Computer Vision, Convolutional Neural Networks, Genetic Algorithms etc. to produce the best possible investment ideas. We have over 2 years of track record of producing exceptional results and outlier win rates for our investors. Why this role matters Model ownership, end-to-end: You’ll design, train, validate, and deploy production-grade models that directly drive portfolio decisions and P&L. Cutting-edge R&D: Work on entropy-based initializers, hybrid CNN/Transformer architectures, and Optuna-driven hyper-parameter searches—ideas that rarely escape academia. Massive impact: A single improvement in signal quality or latency can unlock millions in incremental alpha. Key Responsibilities Area What You’ll Own Research & Signal Discovery Ideate and prototype novel features from tick-level, fundamental, and alternative data; explore quantum-inspired ordering, autoencoders, time-series augmentations. Model Development Build, iterate, and benchmark CNNs, Transformers, GNNs, and Mixture-of-Experts using TensorFlow/PyTorch ; run large-scale Optuna sweeps on our GPU cluster. Deployment & MLOps Package models as Docker images, publish to our Kubernetes-based inference platform, and write CI/CD tests to ensure reproducible builds and seamless rollbacks. Performance Monitoring Create dashboards for live AUC-ROC, precision-recall, drawdown, beta, and slippage; set alert thresholds and conduct post-mortems when KPIs drift. Cross-functional Collaboration Partner with quant researchers, portfolio managers, and full-stack engineers to push models from Jupyter notebooks into live trading strategies. Documentation & Knowledge Sharing Draft technical memos, run code-reviews, and mentor junior analysts on best practices in robust ML for finance. Must-Have Qualifications 3 – 6 years building and shipping ML models in production (preferably finance, ad-tech, or other latency-sensitive domains). Expert-level Python plus strong grasp of TensorFlow or PyTorch ; comfortable profiling GPU memory and optimizing kernels. Proven experience with model deployment (Docker, Kubernetes, CI/CD, feature stores, model registries). Solid understanding of statistics, probability, and time-series analysis; able to articulate bias-variance trade-offs and back-test pitfalls. Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field; a Master’s degree is preferred. Hands-on with SQL and at least one distributed data tool (Spark, Dask, or Ray). Clear, concise communicator—capable of translating technical findings into investor-friendly language. Nice-to-Have Extras Familiarity with trading venues, order-book dynamics, and transaction-cost analysis. Contributions to open-source ML libraries or finance-related GitHub projects. Experience with Optuna, genetic algorithms, or Bayesian optimization frameworks. Knowledge of Von Neumann entropy, quantum-inspired computing, or information-theoretic model selection. Comfort working in a start-up-style, high-ownership culture (you build it, you run it) What We Offer Work with best brains in Data Science and cutting edge modelling to solve complex finance problems Competitive salary + performance bonus tied to desk P&L. ESOP after probation. Comprehensive health insurance (self & family). Annual professional-development budget (courses, conferences, journals). 18 days paid vacation + flexible wellness leave.  How to Apply 1.Email Smriti5991@gmail.com with subject line “Data Scientist — Your Name”. 2.Attach your CV (≤ 2 pages) and link to a GitHub repo or notebook showcasing a project you took all the way to deployment . 3.Short-listed candidates complete a programming test (feature engineering, model training, deployment script) followed by two technical interviews and a culture chat with the founders. We hire for curiosity, rigor, and ownership. If you’re excited by the idea of shipping ML that lives (and profits) in the wild, we’d love to meet you.

hackajob is collaborating with J.P. Morgan to connect them with exceptional tech professionals for this role. We're seeking top talents for our AI engineering team to develop high-quality machine learning models, services, and scalable data processing pipelines. Candidates should have a strong computer science background and be ready to handle end-to-end projects, focusing on engineering. As an Applied AI ML Lead within the Digital Intelligence team at JPMorgan, collaborate with all lines of business and functions to deliver software solutions. Experiment, develop, and productionize high-quality machine learning models, services, and platforms to make a significant impact on technology and business. Design and implement highly scalable and reliable data processing pipelines. Perform analysis and insights to promote and optimize business results. Contribute to a transformative journey and make a substantial impact on a wide range of customer products. Job Responsibilities Research, develop, and implement machine learning algorithms to solve complex problems related to personalized financial services in retail and digital banking domains. Work closely with cross-functional teams to translate business requirements into technical solutions and drive innovation in banking products and services. Collaborate with product managers, key business stakeholders, engineering, and platform partners to lead challenging projects that deliver cutting-edge machine learning-driven digital solutions. Conduct research to develop state-of-the-art machine learning algorithms and models tailored to financial applications in personalization and recommendation spaces. Design experiments, establish mathematical intuitions, implement algorithms, execute test cases, validate results, and productionize highly performant, scalable, trustworthy, and often explainable solutions. Collaborate with data engineers and product analysts to preprocess and analyze large datasets from multiple sources. Stay up-to-date with the latest publications in relevant Machine Learning domains and find applications for the same in your problem spaces for improved outcomes. Communicate findings and insights to stakeholders through presentations, reports, and visualizations. Work with regulatory and compliance teams to ensure that machine learning models adhere to standards and regulations. Mentor Junior Machine Learning associates in delivering successful projects and building successful careers in the firm. Participate and contribute back to firm-wide Machine Learning communities through patenting, publications, and speaking engagements. Required Qualifications, Capabilities And Skills Expert in at least one of the following areas: Natural Language Processing, Knowledge Graph, Computer Vision, Speech Recognition, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis. Deep knowledge in Data structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, Statistics. Demonstrated expertise in machine learning frameworks: Tensorflow, Pytorch, pyG, Keras, MXNet, Scikit-Learn. Strong programming knowledge of python, spark; Strong grasp on vector operations using numpy, scipy; Strong grasp on distributed computation using Multithreading, Multi GPUs, Dask, Ray, Polars etc. Strong analytical and critical thinking skills for problem solving. Excellent written and oral communication along with demonstrated teamwork skills. Demonstrated ability to clearly communicate complex technical concepts to both technical and non-technical audiences Experience in working in interdisciplinary teams and collaborating with other researchers, engineers, and stakeholders. A strong desire to stay updated with the latest advancements in the field and continuously improve one's skills Preferred Qualification, Capabilities And Skills Deep hands-on experience with real-world ML projects, either through academic research, internships, or industry roles. Experience with distributed data/feature engineering using popular cloud services like AWS EMR Experience with large scale training, validation and testing experiments. Experience with cloud Machine Learning services in AWS like Sagemaker. Experience with Container technology like Docker, ECS etc. Experience with Kubernetes based platform for Training or Inferencing. Contributions to open-source ML projects can be a plus. Participation in ML competitions (e.g., Kaggle) and hackathons demonstrating practical skills and problem-solving abilities. Understanding of how ML can be applied to various domains like healthcare, finance, robotics, etc.

You should have at least 2 years of experience as a Python Developer with a robust portfolio of projects. A Bachelor's degree in computer science, Software Engineering, or a related field is required for this role. An in-depth understanding of Python software development stacks, ecosystems, frameworks, and tools like Numpy, Scipy, Pandas, Dask, spaCy, NLTK, sci-kit-learn, and PyTorch is essential. Additionally, you should have experience in front-end development using HTML, CSS, and JavaScript, along with familiarity with database technologies such as SQL and NoSQL. Strong problem-solving skills, as well as effective communication and collaboration abilities, are also necessary. Preferred skills for this role include experience with the Flask framework, a working understanding of cloud platforms like AWS, Google Cloud, or Azure, and contributions to open-source Python projects or active involvement in the Python community.,

What You'll Work On Develop state-of-the-art time series models for anomaly detection and forecasting in observability data. Design a root cause analysis system using LLMs, causal analysis, machine learning and anomaly detection algorithms. Develop Large Language Models for time series analysis Create highly scalable ML pipelines for real-time monitoring and alerting. Build and maintain ML Ops workflows for model deployment, evaluation, monitoring, and updates. Build frameworks to evaluate AI agents. Handle large datasets using Python and its ML ecosystem (e.g., NumPy, Pandas, Scikit-Learn, TensorFlow, PyTorch, Statsmodels). Use Bayesian methods, Granger causality, counterfactual analysis, and other techniques to derive meaningful system insights. Collaborate with other teams to deploy ML-driven observability solutions in production. What We're Looking For 5+ years of hands-on experience in Machine Learning, Time Series Analysis, and Causal Analytics. Bachelors degree in Computer Science, Mathematics or Statistics. Masters or PhD is a plus Strong proficiency in Python and libraries like Scikit-Learn, TensorFlow, PyTorch, Statsmodels, Prophet, or similar. Deep understanding of time-series modeling, forecasting, and anomaly detection techniques. Expertise in causal inference, Bayesian statistics, and causal graph modeling. Experience in ML Ops, model evaluation, and deployment in production environments. Working knowledge of databases and data processing frameworks (SQL, Spark, Dask, etc.). Experience in observability, monitoring, or AI-driven system diagnostics is a big plus. Background in AI Agent evaluation and optimization is a plus. Working with LLMs, fine-tuning LLMs, LLM Ops, LLM Agents is a big plus Our Values Loyalty & Long-term Commitment - We invest in people who invest in us. Opinionated yet Open-Minded - We value strong perspectives but encourage constructive discussions. Passion - We seek individuals who are passionate about their craft. Humility & Integrity - Honest, transparent, and accountable team members are key. Adaptability & Self-Sufficiency - Ability to thrive in a fast-paced and evolving environment. Build Fast and Break Fast - We believe in rapid iteration and learning from failures. What You'll Work On You will be instrumental in building the next-generation Observability platform for automated Root Cause Analysis using LLMs, Machine Learning algorithms. You will be innovating on building LLMs for time series analysis. You'll have the opportunity to work with an experienced team, gain deep insights into how startups are built, and be at the forefront of disruptive innovation in Observability. Comp - upto 2Cr+ (ref:hirist.tech)

About Logik Are you driven to innovate Are you energized by the excitement of building a high-growth startup with winning technology and proven product-market fit Are you looking to join a team of A-players who keep customers first and take their work but not themselves seriously Logik was founded in 2021 by the godfathers of CPQ our CEO Christopher Shutts and our Executive Chairman Godard Abel, who together co-founded BigMachines, the first-ever CPQ technology vendor, in the early 2000s. Today, were reimagining what CPQ can and should be with our composable, AI-enabled platform that provides advanced configuration, transaction management, guided selling, and more. Were a well-funded and fast-growing startup disrupting the CPQ space, with founders that created the category and a platform thats pushing boundaries in configure-price-quote and complex commerce. We're looking for an exceptional AI Backend Engineer to join our Bangalore team and help us build the next generation of AI-powered solutions. Position Summary: As an Senior Backend Engineer AI & ML Specialization Engineer, you will play a crucial role in designing and developing scalable, high-performance backend systems that support our AI models and data pipelines. You will work closely with data scientists, machine learning engineers, and other backend developers to ensure our platform delivers reliable, real-time insights and predictions. Key Responsibilities: Design and develop robust, scalable backend services and APIs that handle large volumes of data and traffic. Implement data ingestion and processing pipelines to efficiently collect, store, and transform data for AI models. Develop and maintain efficient data storage solutions, including databases and data warehouses. Optimize backend systems for performance, scalability, and security. Collaborate with data scientists and machine learning engineers to integrate AI models into backend infrastructure. Collaborate with Devops to implement ML Ops and integrate the models and data engineering pipelines into highly available and reliable tech stacks. Troubleshoot and resolve technical issues related to backend systems and data pipelines. Stay up-to-date with the latest advancements in backend technologies and AI. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. 6+ years of experience in backend development, with a focus on machine learning. Strong proficiency in Python and experience with popular frameworks such as Flask, Django, or FastAPI. Experience with SQL and NoSQL databases such as PostgreSQL, MySQL, MongoDB, or Redis. Experience with cloud platforms such as AWS, Azure, or GCP. Knowledge of date engineering, data pipelines and data processing frameworks such as Apache Airflow, Apache Spark, or Dask. Knowledge of ML Ops frameworks such as Kubeflow and experience with containerisation technologies such as Docker and Kubernetes. Knowledge of distributed computing and parallel programming. Excellent communication and problem-solving skills. Ability to work independently and as part of a team. Preferred Skills: Understanding of AI concepts and machine learning frameworks (e.g., TensorFlow, PyTorch) is a plus. 3 + Years of experience with Java or Go is a plus. Experience with real-time data processing and streaming technologies. What We Offer: Competitive salary and benefits package. Opportunity to work on cutting-edge AI projects. Collaborative and supportive work environment. Continuous learning and professional development opportunities.,

We are looking for an enthusiastic AI/ML Developer with 3-5 years of experience to design, develop, and deploy AI/ML solutions. The ideal candidate is passionate about AI, skilled in machine learning, deep learning, and MLOps, and eager to work on cutting-edge projects. Key Skills & Experience: Programming: Python (TensorFlow, PyTorch, Scikit-learn, Pandas). Machine Learning: Supervised, Unsupervised, Deep Learning, NLP, Computer Vision. Model Deployment: Flask, FastAPI, AWS SageMaker, Google Vertex AI, Azure ML. MLOps & Cloud: Docker, Kubernetes, MLflow, Kubeflow, CI/CD pipelines. Big Data & Databases: Spark, Dask, SQL, NoSQL (PostgreSQL, MongoDB). Soft Skills: Strong analytical and problem-solving mindset. Passion for AI innovation and continuous learning. Excellent teamwork and communication abilities. Qualifications: Bachelor's/Master's in Computer Science, AI, Data Science, or related fields. AI/ML certifications are a plus. Career Level - IC4 We are looking for an enthusiastic AI/ML Developer with 3-5 years of experience to design, develop, and deploy AI/ML solutions. The ideal candidate is passionate about AI, skilled in machine learning, deep learning, and MLOps, and eager to work on cutting-edge projects. Key Skills & Experience: Programming: Python (TensorFlow, PyTorch, Scikit-learn, Pandas). Machine Learning: Supervised, Unsupervised, Deep Learning, NLP, Computer Vision. Model Deployment: Flask, FastAPI, AWS SageMaker, Google Vertex AI, Azure ML. MLOps & Cloud: Docker, Kubernetes, MLflow, Kubeflow, CI/CD pipelines. Big Data & Databases: Spark, Dask, SQL, NoSQL (PostgreSQL, MongoDB). Soft Skills: Strong analytical and problem-solving mindset. Passion for AI innovation and continuous learning. Excellent teamwork and communication abilities. Qualifications: Bachelor's/Master's in Computer Science, AI, Data Science, or related fields. AI/ML certifications are a plus. Diversity & Inclusion: An Oracle career can span industries, roles, Countries and cultures, giving you the opportunity to flourish in new roles and innovate, while blending work life in. Oracle has thrived through 40+ years of change by innovating and operating with integrity while delivering for the top companies in almost every industry. In order to nurture the talent that makes this happen, we are committed to an inclusive culture that celebrates and values diverse insights and perspectives, a workforce that inspires thought leadership and innovation. . Oracle offers a highly competitive suite of Employee Benefits designed on the principles of parity, consistency, and affordability. The overall package includes certain core elements such as Medical, Life Insurance, access to Retirement Planning, and much more. We also encourage our employees to engage in the culture of giving back to the communities where we live and do business. At Oracle, we believe that innovation starts with diversity and inclusion and to create the future we need talent from various backgrounds, perspectives, and abilities. We ensure that individuals with disabilities are provided reasonable accommodation to successfully participate in the job application, interview process, and in potential roles. to perform crucial job functions. That's why we're committed to creating a workforce where all individuals can do their best work. It's when everyone's voice is heard and valued that we're inspired to go beyond what's been done before.

Job Description We're seeking top talents for our AI engineering team to develop high-quality machine learning models, services, and scalable data processing pipelines. Candidates should have a strong computer science background and be ready to handle end-to-end projects, focusing on engineering. As an Applied AI ML Lead within the Digital Intelligence team at JPMorgan, collaborate with all lines of business and functions to deliver software solutions. Experiment, develop, and productionize high-quality machine learning models, services, and platforms to make a significant impact on technology and business. Design and implement highly scalable and reliable data processing pipelines. Perform analysis and insights to promote and optimize business results. Contribute to a transformative journey and make a substantial impact on a wide range of customer products. Job Responsibilities Research, develop, and implement machine learning algorithms to solve complex problems related to personalized financial services in retail and digital banking domains. Work closely with cross-functional teams to translate business requirements into technical solutions and drive innovation in banking products and services. Collaborate with product managers, key business stakeholders, engineering, and platform partners to lead challenging projects that deliver cutting-edge machine learning-driven digital solutions. Conduct research to develop state-of-the-art machine learning algorithms and models tailored to financial applications in personalization and recommendation spaces. Design experiments, establish mathematical intuitions, implement algorithms, execute test cases, validate results, and productionize highly performant, scalable, trustworthy, and often explainable solutions. Collaborate with data engineers and product analysts to preprocess and analyze large datasets from multiple sources. Stay up-to-date with the latest publications in relevant Machine Learning domains and find applications for the same in your problem spaces for improved outcomes. Communicate findings and insights to stakeholders through presentations, reports, and visualizations. Work with regulatory and compliance teams to ensure that machine learning models adhere to standards and regulations. Mentor Junior Machine Learning associates in delivering successful projects and building successful careers in the firm. Participate and contribute back to firm-wide Machine Learning communities through patenting, publications, and speaking engagements. Required Qualifications, Capabilities And Skills Expert in at least one of the following areas: Natural Language Processing, Knowledge Graph, Computer Vision, Speech Recognition, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis. Deep knowledge in Data structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, Statistics. Demonstrated expertise in machine learning frameworks: Tensorflow, Pytorch, pyG, Keras, MXNet, Scikit-Learn. Strong programming knowledge of python, spark; Strong grasp on vector operations using numpy, scipy; Strong grasp on distributed computation using Multithreading, Multi GPUs, Dask, Ray, Polars etc. Strong analytical and critical thinking skills for problem solving. Excellent written and oral communication along with demonstrated teamwork skills. Demonstrated ability to clearly communicate complex technical concepts to both technical and non-technical audiences Experience in working in interdisciplinary teams and collaborating with other researchers, engineers, and stakeholders. A strong desire to stay updated with the latest advancements in the field and continuously improve one's skills Preferred Qualification, Capabilities And Skills Deep hands-on experience with real-world ML projects, either through academic research, internships, or industry roles. Experience with distributed data/feature engineering using popular cloud services like AWS EMR Experience with large scale training, validation and testing experiments. Experience with cloud Machine Learning services in AWS like Sagemaker. Experience with Container technology like Docker, ECS etc. Experience with Kubernetes based platform for Training or Inferencing. Contributions to open-source ML projects can be a plus. Participation in ML competitions (e.g., Kaggle) and hackathons demonstrating practical skills and problem-solving abilities. Understanding of how ML can be applied to various domains like healthcare, finance, robotics, etc. ABOUT US

Job Description We're seeking top talents for our AI engineering team to develop high-quality machine learning models, services, and scalable data processing pipelines. Candidates should have a strong computer science background and be ready to handle end-to-end projects, focusing on engineering. As an Applied AI ML Senior Associate within the Digital Intelligence team at JPMorgan, collaborate with all lines of business and functions to deliver software solutions. Experiment, develop, and productionize high-quality machine learning models, services, and platforms to make a significant impact on technology and business. Design and implement highly scalable and reliable data processing pipelines. Perform analysis and insights to promote and optimize business results. Contribute to a transformative journey and make a substantial impact on a wide range of customer products. Job Responsibilities Research, develop, and implement machine learning algorithms to solve complex problems related to personalized financial services in retail and digital banking domains. Work closely with cross-functional teams to translate business requirements into technical solutions and drive innovation in banking products and services. Collaborate with product managers, key business stakeholders, engineering, and platform partners to lead challenging projects that deliver cutting-edge machine learning-driven digital solutions. Conduct research to develop state-of-the-art machine learning algorithms and models tailored to financial applications in personalization and recommendation spaces. Design experiments, establish mathematical intuitions, implement algorithms, execute test cases, validate results, and productionize highly performant, scalable, trustworthy, and often explainable solutions. Collaborate with data engineers and product analysts to preprocess and analyze large datasets from multiple sources. Stay up-to-date with the latest publications in relevant Machine Learning domains and find applications for the same in your problem spaces for improved outcomes. Communicate findings and insights to stakeholders through presentations, reports, and visualizations. Work with regulatory and compliance teams to ensure that machine learning models adhere to standards and regulations. Mentor Junior Machine Learning associates in delivering successful projects and building successful careers in the firm. Participate and contribute back to firm-wide Machine Learning communities through patenting, publications, and speaking engagements. Required Qualifications, Capabilities And Skills Expert in at least one of the following areas: Natural Language Processing, Knowledge Graph, Computer Vision, Speech Recognition, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis. Deep knowledge in Data structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, Statistics. Demonstrated expertise in machine learning frameworks: Tensorflow, Pytorch, pyG, Keras, MXNet, Scikit-Learn. Strong programming knowledge of python, spark; Strong grasp on vector operations using numpy, scipy; Strong grasp on distributed computation using Multithreading, Multi GPUs, Dask, Ray, Polars etc. Strong analytical and critical thinking skills for problem solving. Excellent written and oral communication along with demonstrated teamwork skills. Demonstrated ability to clearly communicate complex technical concepts to both technical and non-technical audiences Experience in working in interdisciplinary teams and collaborating with other researchers, engineers, and stakeholders. A strong desire to stay updated with the latest advancements in the field and continuously improve one's skills Preferred Qualification, Capabilities And Skills Deep hands-on experience with real-world ML projects, either through academic research, internships, or industry roles. Experience with distributed data/feature engineering using popular cloud services like AWS EMR Experience with large scale training, validation and testing experiments. Experience with cloud Machine Learning services in AWS like Sagemaker. Experience with Container technology like Docker, ECS etc. Experience with Kubernetes based platform for Training or Inferencing. Contributions to open-source ML projects can be a plus. Participation in ML competitions (e.g., Kaggle) and hackathons demonstrating practical skills and problem-solving abilities. Understanding of how ML can be applied to various domains like healthcare, finance, robotics, etc. ABOUT US

We are looking for a talented Python AI/ML Developer with a strong foundation in machine learning, natural language processing, and data science. If you have a passion for building smart, conversational AI systems and want to work on cutting-edge technology, we’d love to hire you! Benefits 5 Days a Week Health Insurance Flexible Timings Open Work Culture Workshops & Webinars Awards & Recognition Festive Celebrations Key Responsibilities Advanced Model Development: Design and implement cutting-edge deep learning models using frameworks like PyTorch and TensorFlow to address specific business challenges. AI Agent and Chatbot Development: Create conversational AI agents capable of delivering seamless, human-like interactions, from foundational models to fine-tuning chatbots tailored to client needs. Retrieval-Augmented Generation (RAG): Develop and optimize RAG models, enhancing AI’s ability to retrieve and synthesize relevant information for accurate responses. Framework Expertise: Leverage LLAMAIndex and LangChain frameworks for building agent-driven applications that interact with large language models (LLMs). Data Infrastructure: Expertise in managing and utilizing data lakes, data warehouses (including Snowflake), and Databricks for large-scale data storage and processing. Machine Learning Operations (MLOps): Manage the full lifecycle of machine learning projects, from data preprocessing and feature engineering through model training, evaluation, and deployment, with a solid understanding of MLOps practices. Data Analysis & Insights: Conduct advanced data analysis to uncover actionable insights and support data-driven strategies across the organization. Cross-Functional Collaboration: Partner with cross-departmental stakeholders to align AI initiatives with business needs, developing scalable AI-driven solutions. Mentorship & Leadership: Guide junior data scientists and engineers, fostering innovation, skill growth, and continuous learning within the team. Research & Innovation: Stay at the forefront of AI and deep learning advancements, experimenting with new techniques to improve model performance and enhance business value. Reporting & Visualization: Develop and present reports, dashboards, and visualizations to effectively communicate findings to both technical and non-technical audiences. Cloud-Based AI Deployment: Utilize AWS Bedrock, including tools like Mistral and Anthropic Claude, to deploy and manage AI models at scale, ensuring optimal performance and reliability. Web Framework Integration: Build and deploy AI-powered applications using web frameworks such as Django and Flask, enabling seamless API integration and scalable backend services. Technical Skills Deep Learning & Machine Learning: Extensive hands-on experience with PyTorch, TensorFlow, and scikit-learn, along with large-scale data processing. Programming & Data Engineering: Strong programming skills in Python or R, with knowledge of big data technologies such as Hadoop, Spark, and advanced SQL. Data Infrastructure: Proficiency in managing and utilising data lakes, data warehouses, and Databricks for large-scale data processing and storage. MLOps & Data Handling: Familiar with MLOps and experienced in data handling tools like pandas and dask for efficient data manipulation. Cloud Computing: Advanced understanding of cloud platforms, especially AWS, for scalable AI/ML model deployment. AWS Bedrock: Expertise in deploying models on AWS Bedrock, with tools such as Mistral and Anthropic Claude. AI Frameworks: Skilled in LLAMAIndex and LangChain, with practical experience in agent-based applications. Data Visualization: Proficient in visualization tools like Tableau, Power BI for clear data presentation. Analytical & Communication Skills: Strong problem-solving abilities with the capability to convey complex technical concepts to diverse audiences. Team Collaboration & Leadership: Proven success in collaborative team environments, with experience in mentorship and leading innovative data science projects. Qualifications Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field. Experience : 3-5 years specializing in deep learning, including extensive experience in PyTorch and TensorFlow. Industry Expertise: Experience in finance, manufacturing, healthcare, or retail sectors. Advanced AI Knowledge: Familiarity with reinforcement learning, NLP, and generative models. Location: Ahmedabad Reporting to: Project Manager

JOB DESCRIPTION We're seeking top talents for our AI engineering team to develop high-quality machine learning models, services, and scalable data processing pipelines. Candidates should have a strong computer science background and be ready to handle end-to-end projects, focusing on engineering. As an Applied AI ML Lead within the Digital Intelligence team at JPMorgan, collaborate with all lines of business and functions to deliver software solutions. Experiment, develop, and productionize high-quality machine learning models, services, and platforms to make a significant impact on technology and business. Design and implement highly scalable and reliable data processing pipelines. Perform analysis and insights to promote and optimize business results. Contribute to a transformative journey and make a substantial impact on a wide range of customer products. Job Responsibilities Research, develop, and implement machine learning algorithms to solve complex problems related to personalized financial services in retail and digital banking domains. Work closely with cross-functional teams to translate business requirements into technical solutions and drive innovation in banking products and services. Collaborate with product managers, key business stakeholders, engineering, and platform partners to lead challenging projects that deliver cutting-edge machine learning-driven digital solutions. Conduct research to develop state-of-the-art machine learning algorithms and models tailored to financial applications in personalization and recommendation spaces. Design experiments, establish mathematical intuitions, implement algorithms, execute test cases, validate results, and productionize highly performant, scalable, trustworthy, and often explainable solutions. Collaborate with data engineers and product analysts to preprocess and analyze large datasets from multiple sources. Stay up-to-date with the latest publications in relevant Machine Learning domains and find applications for the same in your problem spaces for improved outcomes. Communicate findings and insights to stakeholders through presentations, reports, and visualizations. Work with regulatory and compliance teams to ensure that machine learning models adhere to standards and regulations. Mentor Junior Machine Learning associates in delivering successful projects and building successful careers in the firm. Participate and contribute back to firm-wide Machine Learning communities through patenting, publications, and speaking engagements. Required qualifications, capabilities and skills Expert in at least one of the following areas: Natural Language Processing, Knowledge Graph, Computer Vision, Speech Recognition, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis. Deep knowledge in Data structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, Statistics. Demonstrated expertise in machine learning frameworks: Tensorflow, Pytorch, pyG, Keras, MXNet, Scikit-Learn. Strong programming knowledge of python, spark; Strong grasp on vector operations using numpy, scipy; Strong grasp on distributed computation using Multithreading, Multi GPUs, Dask, Ray, Polars etc. Strong analytical and critical thinking skills for problem solving. Excellent written and oral communication along with demonstrated teamwork skills. Demonstrated ability to clearly communicate complex technical concepts to both technical and non-technical audiences Experience in working in interdisciplinary teams and collaborating with other researchers, engineers, and stakeholders. A strong desire to stay updated with the latest advancements in the field and continuously improve one's skills Preferred qualification, capabilities and skills Deep hands-on experience with real-world ML projects, either through academic research, internships, or industry roles. Experience with distributed data/feature engineering using popular cloud services like AWS EMR Experience with large scale training, validation and testing experiments. Experience with cloud Machine Learning services in AWS like Sagemaker. Experience with Container technology like Docker, ECS etc. Experience with Kubernetes based platform for Training or Inferencing. Contributions to open-source ML projects can be a plus. Participation in ML competitions (e.g., Kaggle) and hackathons demonstrating practical skills and problem-solving abilities. Understanding of how ML can be applied to various domains like healthcare, finance, robotics, etc. ABOUT US

💻 What You’ll Do: Machine Learning & AI Development Model Development: Train and optimize ML models for carbon sequestration monitoring, geospatial analytics, and predictive weathering rates. Deep Learning: Apply CNNs, transformers, and diffusion models for remote sensing and climate forecasting. Geospatial AI: Build ML-powered GIS tools, land-use change models, and soil mineralization estimations. Data Engineering & MLOps Scalable ML Pipelines: Develop large-scale data pipelines for climate, soil, and geospatial datasets using Airflow, Dask, or Spark. Cloud & Infrastructure: Deploy ML models on AWS, GCP, or Azure using Docker, Kubernetes, and CI/CD workflows. Big Data Processing: Work with satellite, drone, and sensor data for real-time carbon tracking. Geospatial & Climate Data Analysis Remote Sensing: Process data from Sentinel, Landsat, MODIS, LiDAR, integrating with Google Earth Engine (GEE) and QGIS. Geochemistry & Soil Science: Model mineral weathering, CO2 drawdown, and climate resilience impacts. Time-Series & Climate Data: Analyze NOAA, ERA5, CMIP6 datasets for climate pattern detection. 👀 What We’re Looking For: Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field. 3+ years of experience in machine learning, deep learning, or AI development. Python (NumPy, Pandas, PyTorch, TensorFlow, Scikit-learn) Cloud ML & MLOps (AWS, GCP, Azure, Kubernetes, Docker, CI/CD) Geospatial & Remote Sensing (GIS, Google Earth Engine, QGIS, Sentinel/Landsat) Big Data & Pipelines (Airflow, Dask, Spark, ETL, SQL, NoSQL) Deep Learning & Computer Vision (CNNs, Transformers, Self-Supervised Learning) Familiarity with geospatial data, climate modeling, or environmental science is a plus. Strong problem-solving skills and the ability to work in a collaborative team environment. 🔖 Preferred Qualifications: Experience in climate tech, sustainability, or carbon markets. Contributions to open-source ML or environmental science projects. Background in graph neural networks, diffusion models, or self-supervised learning.