1435 Mlflow Jobs - Page 8

Job Title: Site Reliability Engineer Experience: 5 - 8 Years Location: Hyderabad Work Mode: Work From Office (5 Days a Week) Overview: We are seeking a proactive and technically skilled Site Reliability Engineer (SRE-1) with a strong background in Kubernetes and DevOps practices. This role requires a self-starter who is enthusiastic about automation , observability , and enhancing infrastructure reliability. Key Responsibilities: Manage, monitor, and troubleshoot Kubernetes environments in production. Design, implement, and maintain CI/CD pipelines using tools like Jenkins, ArgoCD, and Ansible. Implement and maintain observability solutions (metrics, logs, traces). Automate infrastructure and operational tasks using scripting languages such as Python, Shell, Groovy, or Ansible. Support and optimize ML workflows, including platforms like MLflow and Kubeflow. Collaborate with cross-functional teams to ensure infrastructure scalability, availability, and performance. Qualifications: Strong hands-on experience with Kubernetes and container orchestration. Solid understanding of DevOps tools and practices. Experience with observability platforms (e.g., Prometheus, Grafana, ELK, Datadog). Familiarity with MLflow and Kubeflow is a strong plus. CKS (Certified Kubernetes Security Specialist) certification is preferred. Exposure to Big Data environments is an added advantage. Proficient in scripting with Python, Shell, Groovy, or Ansible. Hands-on experience with tools like Jenkins, Ansible, and ArgoCD.

Job Title : AI/ML Engineer / Junior Data Scientist Location : Bangalore / Pune Experience : 05 Years Employment Type : Full-Time Salary : 5 15 LPA (Based on experience and skillset) About The Role We are looking for a passionate and driven AI/ML Engineer or Junior Data Scientist to join our growing analytics and product team. Youll work closely with senior data scientists, engineers, and business stakeholders to build scalable AI/ML solutions, extract insights from complex datasets, and develop models that improve real-world decision-making. Whether you're a fresher with solid projects or a professional with up to 5 years of experience, if you're enthusiastic about AI/ML and data science, we want to hear from you! Key Responsibilities Collect, clean, preprocess, and analyze structured and unstructured data from multiple sources. Design, implement, and evaluate machine learning models for classification, regression, clustering, NLP, or recommendation systems. Collaborate with data engineers to deploy models in production (using Python, APIs, or cloud services like AWS/GCP). Visualize results and present actionable insights through dashboards, reports, and presentations. Conduct experiments, hypothesis testing, and A/B tests to optimize models and business outcomes. ? Develop scripts and reusable tools for automation and scalability of ML pipelines. Stay updated with the latest research papers, open-source tools, and trends in AI/ML. Required Skills & Qualifications Bachelors/Masters degree in Computer Science, Data Science, Mathematics, Statistics, or related fields. Strong Python programming skills with experience in libraries like NumPy, Pandas, Scikit-learn, TensorFlow, or PyTorch. Proficiency in data analysis, visualization (using tools like Matplotlib, Seaborn, Plotly, or Power BI/Tableau). Solid understanding of ML algorithms (linear regression, decision trees, random forests, SVMs, neural networks). Experience with SQL and working with large datasets. Exposure to cloud platforms (AWS, GCP, or Azure) and APIs is a plus. Knowledge of NLP, computer vision, or generative AI models is desirable. Strong problem-solving skills, attention to detail, and ability to work in agile teams. Good To Have (Bonus Points) Experience in end-to-end ML model lifecycle (development to deployment). Experience with MLOps tools like MLflow, Docker, or CI/CD. Participation in Kaggle competitions or open-source contributions. Certifications in Data Science, AI/ML, or Cloud Platforms. What We Offer A dynamic and collaborative work environment. Opportunities to work on cutting-edge AI projects. Competitive salary and growth path. Training, mentorship, and access to tools and resources. Flexible work culture and supportive teams. (ref:hirist.tech)

As the MLOps Engineering Director at Horizontal Data Science Enablement Team within SSO Data Science, you will play a crucial role in managing the Databricks platform for the entire organization and leading best practices in MLOps. Your responsibilities will include overseeing the administration, configuration, and maintenance of Databricks clusters and workspaces. You will continuously monitor the clusters for high workloads or excessive usage costs, ensuring the overall health of the clusters and addressing any issues promptly. Implementing and managing security protocols to safeguard sensitive information and facilitating the integration of various data sources into Databricks will be key aspects of your role. Collaborating closely with data engineers, data scientists, and stakeholders, you will provide support for data processing and analytics needs. Maintaining comprehensive documentation of Databricks configurations, processes, and best practices, as well as leading participation in security and architecture reviews, will be part of your responsibilities. Additionally, you will bring MLOps expertise to the table by focusing on areas such as model monitoring, feature catalog/store, model lineage maintenance, and CI/CD pipelines. To excel in this role, you should possess a Master's degree in computer science or a related field, along with strong experience in Databricks management, cloud technologies, and MLOps solutions like MLFlow. Your background should include hands-on experience with industry-standard CI/CD tools, data governance processes, and coding proficiency in languages such as Python, Java, and C++. A systematic problem-solving approach, excellent communication skills, and a sense of ownership and drive are essential qualities for success in this position. Moreover, your ability to set yourself apart will be demonstrated through your experience in SQL tuning, automation, data observability, and supporting highly scalable systems. Operating in a 24x7 environment, self-motivation, creativity in solving software problems, and ensuring system availability across global time zones will further enhance your profile for this role. In alignment with Mastercard's corporate security responsibility, you will be expected to adhere to security policies and practices, maintain the confidentiality and integrity of accessed information, report any security violations, and complete mandatory security trainings. By taking on this role, you will contribute to ensuring the efficiency and security of Mastercard's data science operations.,

You are a highly experienced Senior Python & AI Engineer who will be responsible for leading the development of cutting-edge AI/ML solutions. Your role will involve architecting solutions, driving technical strategy, mentoring team members, and ensuring timely delivery of key projects. As a Technical Leader, you will architect, design, and implement scalable AI/ML systems and backend services using Python. You will also oversee the design and development of machine learning pipelines, APIs, and model deployment workflows. Your responsibilities will include reviewing code, establishing best practices, and driving technical quality across the team. In terms of Team Management, you will lead a team of data scientists, ML engineers, and Python developers. Providing mentorship, coaching, and performance evaluations will be vital. You will facilitate sprint planning, daily stand-ups, and retrospectives using Agile/Scrum practices. Additionally, coordinating with cross-functional teams such as product, QA, DevOps, and UI/UX will be necessary to deliver features on time. Your focus on AI/ML Development will involve developing and fine-tuning models for NLP, computer vision, or structured data analysis based on project requirements. Optimizing model performance and inference using frameworks like PyTorch, TensorFlow, or Hugging Face will be part of your responsibilities. Implementing model monitoring, drift detection, and retraining strategies will also be crucial. Project & Stakeholder Management will require you to work closely with product managers to translate business requirements into technical deliverables. You will own the end-to-end delivery of features, ensuring they meet performance and reliability goals. Providing timely updates to leadership and managing client communication if necessary will also be part of your role. Your Required Skills & Experience include a minimum of 5 years of professional experience with Python and at least 2 years working on AI/ML projects. You should have a strong understanding of ML/DL concepts, algorithms, and data preprocessing. Experience with frameworks like PyTorch, TensorFlow, scikit-learn, FastAPI/Django/Flask, deployment tools like Docker, Kubernetes, MLflow, and cloud platforms such as AWS/GCP/Azure is essential. In terms of leadership, you should have at least 3 years of experience in leading engineering or AI teams, along with excellent planning, estimation, and people management skills. Strong communication and collaboration skills are also required. Preferred Qualifications for this role include a Masters or PhD in Computer Science, Data Science, AI/ML, or related fields, exposure to MLOps practices, experience with RAG, LLMs, transformers, or vector databases, and prior experience in fast-paced startup or product environments. In return, you will have the opportunity to lead cutting-edge AI initiatives, encounter task variety and challenging opportunities, benefit from high autonomy, a flat hierarchy, and fast decision-making, as well as receive competitive compensation and performance-based incentives.,

Job Description Alimentation Couche-Tard Inc., (ACT) is a global Fortune 200 company. A leader in the convenience store and fuel space, it has footprint across 31 countries and territories. Circle K India Data & Analytics team is an integral part of ACT’s Global Data & Analytics Team, and the Data Scientist will be a key player on this team that will help grow analytics globally at ACT. The hired candidate will partner with multiple departments, including Global Marketing, Merchandising, Global Technology, and Business Units. About The Role The incumbent will be responsible for delivering advanced analytics projects that drive business results including interpreting business, selecting the appropriate methodology, data cleaning, exploratory data analysis, model building, and creation of polished deliverables. Responsibilities Analytics & Strategy Analyse large-scale structured and unstructured data; develop deep-dive analyses and machine learning models in retail, marketing, merchandising, and other areas of the business Utilize data mining, statistical and machine learning techniques to derive business value from store, product, operations, financial, and customer transactional data Apply multiple algorithms or architectures and recommend the best model with in-depth description to evangelize data-driven business decisions Utilize cloud setup to extract processed data for statistical modelling and big data analysis, and visualization tools to represent large sets of time series/cross-sectional data Operational Excellence Follow industry standards in coding solutions and follow programming life cycle to ensure standard practices across the project Structure hypothesis, build thoughtful analyses, develop underlying data models and bring clarity to previously undefined problems Partner with Data Engineering to build, design and maintain core data infrastructure, pipelines and data workflows to automate dashboards and analyses Stakeholder Engagement Working collaboratively across multiple sets of stakeholders – Business functions, Data Engineers, Data Visualization experts to deliver on project deliverables Articulate complex data science models to business teams and present the insights in easily understandable and innovative formats Job Requirements Education Bachelor’s degree required, preferably with a quantitative focus (Statistics, Business Analytics, Data Science, Math, Economics, etc.) Master’s degree preferred (MBA/MS Computer Science/M.Tech Computer Science, etc.) Relevant Experience 3–4 years of relevant working experience in a data science/advanced analytics role Behavioural Skills Delivery Excellence Business disposition Social intelligence Innovation and agility Knowledge Functional Analytics (Supply chain analytics, Marketing Analytics, Customer Analytics) Statistical modelling using Analytical tools (R, Python, KNIME, etc.) and use big data technologies Knowledge of statistics and experimental design (A/B testing, hypothesis testing, causal inference) Practical experience building scalable ML models, feature engineering, model evaluation metrics, and statistical inference. Practical experience deploying models using MLOps tools and practices (e.g., MLflow, DVC, Docker, etc.) Strong coding proficiency in Python (Pandas, Scikit-learn, PyTorch/TensorFlow, etc.) Big data technologies & framework (AWS, Azure, GCP, Hadoop, Spark, etc.) Enterprise reporting systems, relational (MySQL, Microsoft SQL Server etc.), non-relational (MongoDB, DynamoDB) database management systems and Data Engineering tools Business intelligence & reporting (Power BI, Tableau, Alteryx, etc.) Microsoft Office applications (MS Excel, etc.)

As a Subject Matter Expert in ML or LLM Ops at CitiusTech, you will play a crucial role in guiding the organization's MLOps and LLMOps strategy. Your responsibilities will include providing strategic direction, technical leadership, and overseeing the development of scalable infrastructure and processes to ensure alignment with business objectives. Your expertise will be instrumental in designing and implementing robust pipelines for ML and LLM models, focusing on efficient deployment and continuous optimization. You will collaborate with cross-functional teams to seamlessly integrate ML and LLM models into production workflows and communicate complex technical concepts clearly to both technical and non-technical audiences. The ideal candidate should have a Bachelor's or Master's degree in Computer Science, Data Science, or a related field, or equivalent experience, with a minimum of 10-15 years of experience in MLOps or LLM Ops. Proficiency in MLOps tools and platforms such as Kubernetes, Docker, Jenkins, Git, MLflow, and LLM-specific tooling is essential. Experience with Cloud platforms like AWS, Azure, Google Cloud, and infrastructure as code principles is desired. In addition to technical skills, excellent communication, collaboration, and problem-solving abilities are crucial for this role. A passion for innovation, the ability to translate technical concepts into clear language, and a drive to optimize ML and LLM workflows are key attributes that we are looking for in potential candidates. CitiusTech offers a dynamic work environment focused on continuous learning, work-life balance, and a culture that values Passion, Respect, Openness, Unity, and Depth (PROUD) of knowledge. Rated as a Great Place to Work, we provide a comprehensive set of benefits to support your career growth and personal well-being. Our EVP, "Be You Be Awesome," reflects our commitment to creating a workplace where employees can thrive personally and professionally. Join CitiusTech to be part of a team that is solving healthcare challenges and positively impacting human lives. Experience faster growth, higher learning, and a stronger impact with us. To know more about CitiusTech and explore career opportunities, visit www.citiustech.com. Happy Applying!,

You are an experienced Lead Data Scientist with a minimum of 8 years of professional experience, including at least 6 years in a data science role. Your expertise lies in statistical modeling, machine learning, deep learning, and GenAI. Proficiency in Python is a must, along with hands-on experience in optimizing code for performance. You excel in data preprocessing, feature engineering, data visualization, hyperparameter tuning, and have a solid understanding of database concepts, especially while working with large datasets. You have experience deploying and scaling machine learning models in a production environment, along with familiarity with machine learning operations (MLOps) and related tools. Your knowledge extends to Generative AI concepts and LLM finetuning, supported by excellent communication and collaboration skills. Your responsibilities as a Lead Data Scientist include guiding and mentoring a high-performance team on the latest technology landscape, patterns, and design standards. You provide strategic direction and technical leadership for AI initiatives, leading the design and architecture of complex AI systems. Your role involves developing and deploying machine learning/deep learning models to address key business challenges, applying various techniques in statistical modeling, data preprocessing, feature engineering, and more. You are proficient in areas such as computer vision, predictive analytics, natural language processing, time series analysis, and recommendation systems. Furthermore, you design and optimize data pipelines for model training and deployment, utilizing model serving frameworks and APIs for integration with other systems. Your qualifications include a Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, computer science, or a related area. Your primary skills encompass Python, Data Science concepts, Pandas, NumPy, Matplotlib, Artificial Intelligence, Statistical Modeling, Machine Learning, Natural Language Processing (NLP), Deep Learning, Model Serving Frameworks, MLOps, Computer Vision, Predictive Analytics, Time Series Analysis, Anomaly Detection, Recommendation Systems, Generative AI, and proficiency in Cloud Computing Platforms. Your secondary skills involve expertise in designing scalable and efficient model architectures, the ability to assess and forecast financial requirements of data science projects, and strong communication skills for conveying technical concepts to stakeholders. As a Lead Data Scientist, you stay updated with the latest advancements in data science and machine learning, particularly in generative AI, to evaluate their potential applications and serve as a primary point of contact for client managers.,

You are a results-driven Data Project Manager (PM) responsible for leading data initiatives within a regulated banking environment, focusing on leveraging Databricks and Confluent Kafka. Your role involves overseeing the successful end-to-end delivery of complex data transformation projects aligned with business and regulatory requirements. In this position, you will be required to lead the planning, execution, and delivery of enterprise data projects using Databricks and Confluent. This includes developing detailed project plans, delivery roadmaps, and work breakdown structures, as well as ensuring resource allocation, budgeting, and adherence to timelines and quality standards. Collaboration with data engineers, architects, business analysts, and platform teams is essential to align on project goals. You will act as the primary liaison between business units, technology teams, and vendors, facilitating regular updates, steering committee meetings, and issue/risk escalations. Your technical oversight responsibilities include managing solution delivery on Databricks for data processing, ML pipelines, and analytics, as well as overseeing real-time data streaming pipelines via Confluent Kafka. Ensuring alignment with data governance, security, and regulatory frameworks such as GDPR, CBUAE, and BCBS 239 is crucial. Risk and compliance management are key aspects of your role, involving ensuring regulatory reporting data flows comply with local and international financial standards and managing controls and audit requirements in collaboration with Compliance and Risk teams. The required skills and experience for this role include 7+ years of Project Management experience within the banking or financial services sector, proven experience in leading data platform projects, a strong understanding of data architecture, pipelines, and streaming technologies, experience in managing cross-functional teams, and proficiency in Agile/Scrum and Waterfall methodologies. Technical exposure to Databricks (Delta Lake, MLflow, Spark), Confluent Kafka (Kafka Connect, kSQL, Schema Registry), Azure or AWS Cloud Platforms, integration tools, CI/CD pipelines, and Oracle ERP Implementation is expected. Preferred qualifications include PMP/Prince2/Scrum Master certification, familiarity with regulatory frameworks, and a strong understanding of data governance principles. The ideal candidate will hold a Bachelors or Masters degree in Computer Science, Information Systems, Engineering, or a related field. Key performance indicators for this role include on-time, on-budget delivery of data initiatives, uptime and SLAs of data pipelines, user satisfaction, and compliance with regulatory milestones.,

At PwC, we focus on leveraging data to drive insights and make informed business decisions in the field of data and analytics. Our team utilises advanced analytics techniques to help clients optimize their operations and achieve their strategic goals. As a Data Analyst at PwC, you will play a crucial role in utilizing advanced analytical techniques to extract insights from large datasets and facilitate data-driven decision-making. Your responsibilities will include leveraging skills in data manipulation, visualization, and statistical modeling to assist clients in solving complex business problems. We are currently seeking a highly skilled MLOps/LLMOps Engineer to join PwC US - Acceleration Center. In this role, you will be responsible for the deployment, scaling, and maintenance of Generative AI models. Working closely with data scientists, ML/GenAI engineers, and DevOps teams, you will ensure seamless integration and operation of GenAI models within production environments at PwC and our clients. The ideal candidate will have a strong background in MLOps practices, coupled with experience and interest in Generative AI technologies. As a candidate, you should have a minimum of 4+ years of hands-on experience. Core qualifications for this role include 3+ years of experience developing and deploying AI models in production environments, with a year of experience in developing proofs of concept and prototypes. Additionally, a strong background in software development, proficiency in programming languages like Python, knowledge of ML frameworks and libraries, familiarity with containerization and orchestration tools, and experience with cloud platforms and CI/CD tools are essential. Key responsibilities of the role involve developing and implementing MLOps strategies tailored for Generative AI models, designing and managing CI/CD pipelines specialized for ML workflows, monitoring and optimizing the performance of AI models in production, collaborating with data scientists and ML researchers, and ensuring compliance with data privacy regulations. You will also be responsible for troubleshooting and resolving issues related to ML model serving, data anomalies, and infrastructure performance. The successful candidate will be proficient in MLOps tools such as MLflow, Kubeflow, Airflow, or similar, have expertise in generative AI frameworks, containerization technologies, MLOps and LLMOps practices, and cloud-based AI services. Nice-to-have qualifications include experience with advanced GenAI applications, familiarity with experiment tracking tools, knowledge of high-performance computing techniques, and contributions to open-source MLOps or GenAI projects. In addition to technical skills, the role requires project delivery capabilities such as designing scalable deployment pipelines for ML/GenAI models, overseeing cloud infrastructure setup, and creating detailed documentation for deployment pipelines. Client engagement is another essential aspect, involving collaboration with clients to understand their business needs, presenting technical approaches and results, conducting training sessions, and creating user guides for clients. To stay ahead in the field, you will need to stay updated with the latest trends in MLOps/LLMOps and Generative AI, apply this knowledge to improve existing systems and processes, develop internal tools and frameworks, mentor junior team members, and contribute to technical publications. The ideal candidate for this position should hold any graduate/BE/B.Tech/MCA/M.Sc/M.E/M.Tech/Masters Degree/MBA. Join us at PwC and be part of a dynamic team driving innovation in data and analytics!,

Weave is looking for engineers hungry for fun challenges who can join our self-empowered teams and contribute in both technical and non-technical ways. You will be joining a team of talented developers that share a common interest in distributed backend systems, data, scalability, and continued development. You will get a chance to apply these, and other skills, to new and ongoing projects to make machine learning more approachable, data more available, and easier to discover and use by helping design how teams build out AI powered features at Weave. Our teams are cross-functional agile teams composed of a product owner, backend and frontend devs and devops. Teams are highly autonomous with the ownership and ability to act in Weave’s best interest. Above all, your work will impact the way our customers experience Weave while working closely with a highly skilled team to accomplish varying goals and cultivate our phenomenal culture. Purpose The Machine Learning Team's mission is to enable product innovation by making it painless for developers to build ai powered applications that require access to large sets of data. Machine learning is challenging but we are striving to democratize access to the tools and technology that powers it so teams can build cutting edge features safely and responsibly without a PhD in Data Science. As a Machine Learning Engineer on the team you’ll be building models for new products with emerging technologies, at scale. We handle data for hundreds of millions of people daily. We currently have two openings available Staff Machine Learning Engineer and Senior Machine Engineer. We will determine level based on experience and technical competencies. This position is fully remote in India You will report to: Director of Engineering What You Will Own Design and Develop machine learning infrastructure, tooling, and models to help teams deliver world class experiences. Help product and development teams understand the data lifecycle and the inherent experimental nature of machine learning. Build internal products and platforms to enable teams to incorporate AI into their features and customer facing products. Consult with teams to help them understand common patterns, anti-patterns, and tradeoffs of machine learning. Guide them through creating excellent customer experiences end to end. Build scalable, resilient services to support data integration, event processing, and platform extensions. Contribute to the continued evolution of product functionality that services large amounts of data and traffic. Write code that is high-quality, performant, sustainable, and testable while holding yourself accountable for the quality of the code you produce. Coach and collaborate inside and outside the team. You enjoy working closely with others - helping them grow by sharing expertise and encouraging best practices. Work in a cloud environment, considering the implementation of functionality through several distributed components and services. Work with our stakeholders to translate product goals into actionable engineering plans. What You Will Need To Accomplish The Job High integrity, team-focused approach, and collaboration skills to build tight-knit relationships across Weave with various roles and stakeholders. Responsive person with a strong bias for action. 5+ years of experience in any structured back-end language, i.e. Go, Java or Python (Go and Python experience is a plus). Experience moving and storing TBs of data or 100M’s to 10B’s of records. Experience building and deploying ML driven B2B multi-tenant applications in production environments. Experience with common ML technologies such as Python, Jupyter, Workflow Engines (Dagster, MLFlow, KubeFlow, etc), DVC, Triton Server, LLMs, Postgres, and others. Experience with modern ML tools and techniques such as LLMs, RAG, Prompt Engineering, Fine Tuning, multi-modal models, and others. Experience with data labelling or annotation for audio or text use cases. Understanding of distributed systems and building scalable, redundant, and observable services. Expertise in designing and architecting systems for distributed data sets and services. Experience building solutions to run on one or more of the public clouds (e.g., AWS, GCP, etc.). Experience providing stable well designed libraries and SDKs for internal use. Self driven and a thirst for learning in a quickly changing industry. Demonstrated track record of delivering complex projects on time and have experience working in enterprise-grade production environments. Strategic thinker with a strong technical aptitude and a passion for execution. What Will Make Us Love You A background with data analysis, visualization, and presentation. 3+ years of experience in data science, machine learning, or predictive analytics in addition to engineering experience. Experience with natural language models, embeddings, and inference in production, at scale. Experience with real-time audio models and voice use cases such as transcription, ASR pipelines with interruption detection, audio alignment, and speech synthesis. Experience with emerging technologies such as Model Context Protocol (MCP). Proficient understanding of containers, orchestrators, and usage patterns at scale including networking, storage, service meshes, and multi-cluster communication. Experience with Kubernetes or GKE and the Operator Pattern (GCP), specifically, a plus. Experience with highly sensitive data such as PHI (HIPAA) and PII data. Experience with automation and container based workflow engines. Experience with GitOps, IaC, and configuration driven systems. A preference for open source solutions. A track record of clean abstractions and simple to use APIs. Deep understanding of distributed data technologies such as streaming, data mesh, data lakes, warehouses, or distributed machine learning. A desire to advance the state of the art with new and innovative technologies. Enjoys working in a greenfield environment using rapid prototyping. Enjoys working with open-ended, evolving problems, and domains. Weave is an equal opportunity employer that is committed to fostering an inclusive workplace where all individuals are valued and supported. We welcome anyone who is hungry to learn, problem-solve and progress regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or other applicable legally protected characteristics. If you have a disability or special need that requires accommodation, please let us know. All official correspondence will occur through Weave branded email. We will never ask you to share bank account information, cash a check from us, or purchase software or equipment as part of your interview or hiring process.

About Position: We are conducting an in-person hiring drive for the position of Mlops / Data Science in Pune & Bengaluru on 19th July 2025.Interview Location is mentioned below: Pune – Persistent Systems, Veda Complex, Rigveda-Yajurveda-Samaveda-Atharvaveda Plot No. 39, Phase I, Rajiv Gandhi Information Technology Park, Hinjawadi, Pune, 411057 Bangalore - Persistent Systems, The Cube at Karle Town Center Rd, DadaMastan Layout, Manayata Tech Park, Nagavara, Bengaluru, Karnataka 560024 We are looking for an experienced and talented Data Science to join our growing data competency team. The ideal candidate will have a strong background in working with GEN AI , ML ,LangChain, LangGraph, Mlops Architecture Strategy, Prompt engineering. You will work closely with our data analysts, engineers, and business teams to ensure optimal performance, scalability, and availability of our data pipelines and analytics. Role: Mlops, Data Science Job Location: All PSL Location Experience: 5+ Years Job Type: Full Time Employment What You'll Do: Design, build, and manage scalable ML model deployment pipelines (CI/CD for ML). Automate model training, validation, monitoring, and retraining workflows. Implement model governance, versioning, and reproducibility best practices. Collaborate with data scientists, engineers, and product teams to operationalize ML solutions. Ensure robust monitoring and performance tuning of deployed models Expertise You'll Bring: Strong experience with MLOps tools & frameworks (MLflow, Kubeflow, SageMaker, Vertex AI, etc.). Proficient in containerization (Docker, Kubernetes). Good knowledge of cloud platforms (AWS, Azure, or GCP). Expertise in Python and familiarity with ML libraries (TensorFlow, PyTorch, scikit-learn). Solid understanding of CI/CD, infrastructure as code, and automation tools. Benefits: Competitive salary and benefits package Culture focused on talent development with quarterly promotion cycles and company-sponsored higher education and certifications Opportunity to work with cutting-edge technologies Employee engagement initiatives such as project parties, flexible work hours, and Long Service awards Annual health check-ups Insurance coverage: group term life, personal accident, and Mediclaim hospitalization for self, spouse, two children, and parents Inclusive Environment: Persistent Ltd. is dedicated to fostering diversity and inclusion in the workplace. We invite applications from all qualified individuals, including those with disabilities, and regardless of gender or gender preference. We welcome diverse candidates from all backgrounds. We offer hybrid work options and flexible working hours to accommodate various needs and preferences. Our office is equipped with accessible facilities, including adjustable workstations, ergonomic chairs, and assistive technologies to support employees with physical disabilities. If you are a person with disabilities and have specific requirements, please inform us during the application process or at any time during your employment. We are committed to creating an inclusive environment where all employees can thrive. Our company fosters a values-driven and people-centric work environment that enables our employees to: Accelerate growth, both professionally and personally Impact the world in powerful, positive ways, using the latest technologies Enjoy collaborative innovation, with diversity and work-life wellbeing at the core Unlock global opportunities to work and learn with the industry’s best Let’s unleash your full potential at Persistent “Persistent is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind.”

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. Position Name ML Developer Taleo ID Position Level Staff Employment Type Permanent Number of Openings 1 Work Location Kochi, Chennai , Noida , Bangalore , Pune , Kolkata , TVM Position Details As part of EY GDS Assurance Digital, you will be responsible for leveraging advanced machine learning techniques to develop innovative, high-impact models and solutions that drive growth and deliver significant business value. You will be helping EY’s sector and service line professionals by developing analytics enabled solutions, integrating data science activities with business relevant aspects to gain insight from data. This is a full-time Machine Learning Developer role, responsible for building and deploying robust machine learning models to solve real-world business problems. You will be working on the entire ML lifecycle, including data analysis, feature engineering, model training, evaluation, and deployment. Requirements (including Experience, Skills And Additional Qualifications) A bachelor’s degree (BE/BTech/MCA & MBA) in Computer Science, Engineering, Information Systems Management, Accounting, Finance or a related field with adequate industry experience. Technical Skills Requirements Develop and implement machine learning models, including regression, classification (e.g., XGBoost, Random Forest), and clustering techniques. Conduct exploratory data analysis (EDA) to uncover insights and trends within data sets. Apply dimension reduction techniques to improve model performance and interpretability. Utilize statistical models to design and implement effective business solutions. Evaluate and validate models to ensure robustness and reliability. Should have solid background in Python Familiarity with Time Series Forecasting. Basic experience with cloud platforms such as AWS, Azure, or GCP. Exposure to ML Ops tools and practices (e.g., MLflow, Airflow, Docker) is a plus Additional skill requirements: Proficient at quickly understanding complex machine learning concepts and utilizing technology for tasks such as data modeling, analysis, visualization, and process automation. Skilled in selecting and applying the most suitable standards, methods, tools, and frameworks for specific ML tasks and use cases. Capable of collaborating effectively within cross-functional teams, while also being able to work independently on complex ML projects. Demonstrates a strong analytical mindset and systematic approach to solving machine learning challenges. Excellent communication skills, able to present complex technical concepts clearly to both technical and non-technical audiences. What we look for A Team of people with commercial acumen, technical experience, and enthusiasm to learn new things in this fast-moving environment. An opportunity to be a part of market-leading, multi-disciplinary team of 7200 + professionals, in the only integrated global assurance business worldwide. Opportunities to work with EY GDS Assurance practices globally with leading businesses across a range of industries What working at EY offers At EY, we’re dedicated to helping our clients, from startups to Fortune 500 companies — and the work we do with them is as varied as they are. You get to work with inspiring and meaningful projects. Our focus is education and coaching alongside practical experience to ensure your personal development. We value our employees, and you will be able to control your own development with an individual progression plan. You will quickly grow into a responsible role with challenging and stimulating assignments. Moreover, you will be part of an interdisciplinary environment that emphasizes high quality and knowledge exchange. Plus, we offer: Support, coaching and feedback from some of the most engaging colleagues around Opportunities to develop new skills and progress your career The freedom and flexibility to handle your role in a way that’s right for you About EY As a global leader in assurance, tax, transaction, and advisory services, we’re using the finance products, expertise, and systems we’ve developed to build a better working world. That starts with a culture that believes in giving you the training, opportunities, and creative freedom to make things better. Whenever you join, however long you stay, the exceptional EY experience lasts a lifetime. And with a commitment to hiring and developing the most passionate people, we’ll make our ambition to be the best employer by 2020 a reality. If you can confidently demonstrate that you meet the criteria above, please contact us as soon as possible. Join us in building a better working world. Apply now EY provides equal employment opportunities to applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status. 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. Position Name ML Developer Taleo ID Position Level Staff Employment Type Permanent Number of Openings 1 Work Location Kochi, Chennai , Noida , Bangalore , Pune , Kolkata , TVM Position Details As part of EY GDS Assurance Digital, you will be responsible for leveraging advanced machine learning techniques to develop innovative, high-impact models and solutions that drive growth and deliver significant business value. You will be helping EY’s sector and service line professionals by developing analytics enabled solutions, integrating data science activities with business relevant aspects to gain insight from data. This is a full-time Machine Learning Developer role, responsible for building and deploying robust machine learning models to solve real-world business problems. You will be working on the entire ML lifecycle, including data analysis, feature engineering, model training, evaluation, and deployment. Requirements (including Experience, Skills And Additional Qualifications) A bachelor’s degree (BE/BTech/MCA & MBA) in Computer Science, Engineering, Information Systems Management, Accounting, Finance or a related field with adequate industry experience. Technical Skills Requirements Develop and implement machine learning models, including regression, classification (e.g., XGBoost, Random Forest), and clustering techniques. Conduct exploratory data analysis (EDA) to uncover insights and trends within data sets. Apply dimension reduction techniques to improve model performance and interpretability. Utilize statistical models to design and implement effective business solutions. Evaluate and validate models to ensure robustness and reliability. Should have solid background in Python Familiarity with Time Series Forecasting. Basic experience with cloud platforms such as AWS, Azure, or GCP. Exposure to ML Ops tools and practices (e.g., MLflow, Airflow, Docker) is a plus Additional skill requirements: Proficient at quickly understanding complex machine learning concepts and utilizing technology for tasks such as data modeling, analysis, visualization, and process automation. Skilled in selecting and applying the most suitable standards, methods, tools, and frameworks for specific ML tasks and use cases. Capable of collaborating effectively within cross-functional teams, while also being able to work independently on complex ML projects. Demonstrates a strong analytical mindset and systematic approach to solving machine learning challenges. Excellent communication skills, able to present complex technical concepts clearly to both technical and non-technical audiences. What we look for A Team of people with commercial acumen, technical experience, and enthusiasm to learn new things in this fast-moving environment. An opportunity to be a part of market-leading, multi-disciplinary team of 7200 + professionals, in the only integrated global assurance business worldwide. Opportunities to work with EY GDS Assurance practices globally with leading businesses across a range of industries What working at EY offers At EY, we’re dedicated to helping our clients, from startups to Fortune 500 companies — and the work we do with them is as varied as they are. You get to work with inspiring and meaningful projects. Our focus is education and coaching alongside practical experience to ensure your personal development. We value our employees, and you will be able to control your own development with an individual progression plan. You will quickly grow into a responsible role with challenging and stimulating assignments. Moreover, you will be part of an interdisciplinary environment that emphasizes high quality and knowledge exchange. Plus, we offer: Support, coaching and feedback from some of the most engaging colleagues around Opportunities to develop new skills and progress your career The freedom and flexibility to handle your role in a way that’s right for you About EY As a global leader in assurance, tax, transaction, and advisory services, we’re using the finance products, expertise, and systems we’ve developed to build a better working world. That starts with a culture that believes in giving you the training, opportunities, and creative freedom to make things better. Whenever you join, however long you stay, the exceptional EY experience lasts a lifetime. And with a commitment to hiring and developing the most passionate people, we’ll make our ambition to be the best employer by 2020 a reality. If you can confidently demonstrate that you meet the criteria above, please contact us as soon as possible. Join us in building a better working world. Apply now EY provides equal employment opportunities to applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status. 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.

Your Future Evolves Here Evolent partners with health plans and providers to achieve better outcomes for people with most complex and costly health conditions. Working across specialties and primary care, we seek to connect the pieces of fragmented health care system and ensure people get the same level of care and compassion we would want for our loved ones. Evolent employees enjoy work/life balance, the flexibility to suit their work to their lives, and autonomy they need to get things done. We believe that people do their best work when they're supported to live their best lives, and when they feel welcome to bring their whole selves to work. That's one reason why diversity and inclusion are core to our business. Join Evolent for the mission. Stay for the culture. What You’ll Be Doing: MLOps Engineer We are seeking a highly capable MLOps Engineer to join our growing AI/ML Team. You will bridge the gap between data science and operations, ensuring that machine learning models are efficiently tested, deployed, monitored, and maintained in production environments. You will work closely with data scientists, software engineers, infrastructure, and development teams to build scalable and reliable ML infrastructure. You will be instrumental in supporting clinical decision-making, operational efficiency, quality outcomes, and patient care. What You Will Be Doing: Model Deployment and Infrastructure Design, build, and maintain scalable, secure ML pipelines for model training, validation, deployment, and monitoring Automate deployment workflows using CI/CD pipelines and infrastructure-as-code tools Partner with Infrastructure Teams to manage (Azure) cloud-based ML infrastructure, ensuring compliance with InfoSec and AI policies Ensure applications run at peak efficiency Model Testing, Monitoring, and Validation Develop rigorous testing frameworks for ML models, including clinical validation, traditional model performance measures, population segmentation, and edge-case analysis Build monitoring systems to detect model drift, overfitting, data anomalies, and performance degradation in real-time Continuously analyze model performance metrics and operational logs to identify improvement opportunities Translate monitoring insights into actionable recommendations for data scientists to improve model precision, recall, fairness, and efficiency Model Transparency & Governance Maintain detailed audit trails, logs, and metadata for all model versions, training datasets, and configurations to ensure full traceability and support internal audits Ensure models meet transparency and explainability standards using tools like SHAP, LIME, or integrated explainability APIs. Collaborate with data scientists and clinical teams to ensure models are interpretable, actionable, and aligned with practical applications Support corporate Compliance and AI Governance policies Advocate for best practices in ML engineering, including reproducibility, version control, and ethical AI Develop product guides, model documentation, and model cards for internal and external stakeholders Required Qualifications : Bachelor’s Degree in Computer Science, Machine Learning, Data Science, or a related field 2+ years of experience in MLOps, DevOps, or ML engineering Proficiency in Python and ML frameworks such as Keras, PyTorch, Scikit-Learn, TensorFlow, and XGBoost Experience with containerization (Docker), orchestration (Kubernetes), and CI/CD tools Familiarity with healthcare datasets and privacy regulations Strong analytical skills to interpret model performance data and identify optimization opportunities Proven ability to optimize application performance, including improving code efficiency, right-sizing infrastructure usage, and reducing system latency Experience implementing rollback strategies, including version control, rollback triggers, and safe deployment practices across lower and upper environments 2+ years of experience developing in a cloud environment (AWS, GCS, Azure) 2+ years of experience with Github, Github Actions, CI/CD, and source control 2+ years working within an Agile environment Preferred Qualifications: Experience with MLOps platforms like MLflow, TFX, or Kubeflow Healthcare experience, particularly using administrative and prior authorization data Proven experience with developing and deploying ML systems into production environments Experience working with Product, Engineering, Infrastructure, and Architecture teams Proficiency using Azure cloud-based services and infrastructure such as Azure MLOps Experience with feature flagging tools and strategies To comply with HIPAA security standards (45 C.F.R. sec. 164.308 (a) (3)), identity verification may be required as part of the application process. This is collected for compliance and security purposes and only reviewed if an applicant advances to the final interview state. Reasonable accommodations are available upon request. Technical Requirements: We require that all employees have the following technical capability at their home: High speed internet over 10 Mbps and, specifically for all call center employees, the ability to plug in directly to the home internet router. These at-home technical requirements are subject to change with any scheduled re-opening of our office locations. Evolent is an equal opportunity employer and considers all qualified applicants equally without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, or disability status. If you need reasonable accommodation to access the information provided on this website, please contact recruiting@evolent.com for further assistance. The expected base salary/wage range for this position is $. This position is also eligible for a bonus component that would be dependent on pre-defined performance factors. As part of our total compensation package, Evolent is proud to offer comprehensive benefits (including health insurance benefits) to qualifying employees. All compensation determinations are based on the skills and experience required for the position and commensurate with experience of selected individuals, which may vary above and below the stated amounts.

Job Description: Designs and implements AI architectures based on client needs Strategically utilizes the in-house data center as a platform for customer AI workloads Collaborates closely with international teams of data scientists, engineers, and infrastructure specialists Aligns AI solutions with compute, storage, and network capacities across private, hybrid, and public infrastructures Makes strategic choices in AI tools and technologies with a focus on open source and scalability Supports MLOps implementations, CI/CD, data pipelines, and lifecycle management Contributes to AI governance, reliability, and solution scalability Keeps up with the latest developments in AI, hardware acceleration, and infrastructure Assists clients in leveraging AI at an enterprise level Requirements: At least 10 years of experience as an AI Architect or in a comparable technical AI role Experience developing AI solutions for clients in an enterprise environment Experience working in an international context with cross-functional teams Experience with AI hardware and solutions from NVIDIA, IBM, and HPE is a plus In-depth knowledge of open-source AI frameworks such as TensorFlow, PyTorch, Hugging Face, MLflow, Kubeflow, etc. Familiarity with containers and orchestration tools like Docker and Kubernetes Understanding of data governance, security, and compliance in AI projects Strong communication skills and a client-oriented mindset Excellent command of the English language, both spoken and written

Overview Lead the design and development of GenAI-powered systems, including LLM apps, RAG pipelines, fine-tuning strategies, and model deployment. Own architectural decisions, code quality, and system scalability in AI-heavy projects. Guide the team in using tools like OpenAI, Hugging Face, LangChain, Pinecone/Weaviate, and model optimization libraries. Manage the full ML lifecycle: from data preparation and model experimentation to deployment and monitoring in production. Collaborate with Product Managers, Designers, and Stakeholders to define and prioritize AI use cases. Coach and mentor team members in technical growth, GenAI trends, and engineering excellence. Stay abreast of the latest GenAI research (e.g., GPT-4/Claude/Mistral/Sora) and translate those insights into practical applications Responsibilities 6+ years of software engineering or ML experience with at least 2+ years leading teams or projects. Hands-on experience with LLMs (GPT, LLaMA, Claude, etc.) and GenAI frameworks (LangChain, LlamaIndex, etc.). Deep proficiency in Python, APIs, ML pipelines, and cloud platforms (AWS/GCP/Azure). Solid understanding of vector databases (FAISS, Pinecone, Weaviate), embeddings, and RAG architecture. Experience with fine-tuning, prompt engineering, and/or RLHF workflows. Excellent communication, leadership, and stakeholder management skills. Requirements Background in NLP, multimodal models (text + image/video/audio), or knowledge distillation. Experience building GenAI chatbots, copilots, or domain-specific AI assistants. Familiarity with DevOps/MLOps: Docker, Kubernetes, MLflow, CI/CD, or model monitoring. Contributions to AI research or open-source GenAI tools/projects.

Sr. Data Scientist (Predictive Analytics Focus & Databricks) – Senior Programmer Analyst Responsibilities: Design and deploy predictive models (e.g., forecasting, churn analysis, fraud detection) using Python/SQL, Spark MLlib, and Databricks ML. Build end-to-end ML pipelines (data ingestion feature engineering model training deployment) on Databricks Lakehouse. Optimize model performance via hyperparameter tuning, AutoML, and MLflow tracking. Collaborate with engineering teams to operationalize models (batch/real-time) using Databricks Jobs or REST APIs. Implement Delta Lake for scalable, ACID-compliant data workflows. Enable CI/CD for ML pipelines using Databricks Repos and GitHub Actions. Troubleshoot issues in Spark Jobs and Databricks Environment. Requirements: Experience should have 3 to 5 years in predictive analytics, with expertise in regression, classification, time-series modeling. Hands-on experience with Databricks Runtime for ML, Spark SQL, and PySpark. Familiarity with MLflow, Feature Store, and Unity Catalog for governance. Industry experience in Life Insurance or P&C. Good to have certification on Databricks Certified ML Practitioner. Technical Skills: Python, PySpark, MLflow, Databricks AutoML. Predictive Modelling (Classification, Clustering, Regression, timeseries and NLP). Cloud platform (Azure/AWS), Delta Lake, Unity Catalog.

Position: Capability Lead – Databricks (Director/ Enterprise Architect) Location: Chennai, Hyderabad, Bangalore or Noida (Hybrid)- No remote option available Duration: Full Time Reporting : Practice Head Budget: 30- 65 LPA (Depending on level of expertise) Notice Period: Immediate Joiner/ Currently Serving/ Notice is less than 60 days Level of experience: 12+ Years Shift Timings: 2 pm -11 pm IST. Overlap with EST time zone of 2 pm Job Summary As part of the leadership team for Data business, the role will be responsible for building and growing the Databricks capability within the organization. The role entails driving technical strategy, innovation, and solution delivery on the Databricks Unified Data Analytics platform. This leader will work closely with clients, delivery teams, technology partners, and internal stakeholders to define and deliver scalable, high-performance solutions using Databricks. Areas of Responsibility 1. Offering and Capability Development Design and enhance Databricks-based solutions, accelerators, and reusable frameworks Define architectural patterns and best practices for Lakehouse implementations Collaborate with Databricks alliance teams to grow partnership and co-sell opportunities 2. Technical Leadership Provide architectural oversight for Databricks engagements including Delta Lake, Unity Catalog, MLflow, and Structured Streaming Lead solution architecture and design in proposals, RFPs, and strategic pursuits Establish technical governance and conduct reviews to ensure standards compliance Act as a technical escalation point for complex use cases in big data and machine learning 3. Delivery Oversight Guide delivery teams through implementation best practices, optimization, and troubleshooting Ensure high-quality execution across Databricks programs with a focus on performance, scalability, and governance Drive consistent use of CI/CD, automation, and monitoring for Databricks workloads 4. Talent Development Build a specialized Databricks talent pool through recruitment, training, and mentoring Define certification and career paths aligned with Databricks and related ecosystem tools Lead internal community of practice sessions and promote knowledge sharing 5. Business Development Support Partner with sales and pre-sales to position Databricks-based analytics and AI/ML solutions Identify new use cases and business opportunities using the Databricks platform Participate in client discussions, workshops, and architecture review boards 6. Thought Leadership and Innovation Develop whitepapers, blogs, and PoVs showcasing advanced analytics and AI/ML use cases on Databricks Stay abreast of Databricks roadmap, product features, and industry developments Drive innovative solutioning using Databricks with modern data stack components Job Requirements 12–15 years of experience in Data Engineering, Analytics, or AI/ML, with 3–5 years of focused experience on Databricks Proficiency in architectural best practices in cloud around user management, data privacy, data security, performance and other non-functional requirements Proven experience delivering large-scale data and AI/ML workloads on Databricks Deep knowledge of Spark, Delta Lake, Python/Scala, SQL, and data pipeline orchestration Experience with MLOps, Feature Store, Unity Catalog, and model lifecycle management Certification preferred (e.g., Lakehouse Fundamentals, Data Engineer Associate/ Professional) Experience integrating Databricks with cloud platforms (Azure, AWS, or GCP) and BI tools Strong background in solution architecture, presales, and client advisory Excellent communication, stakeholder engagement, and leadership skills Exposure to data governance, security, compliance, and cost optimization in cloud analytics About Mastech InfoTrellis Mastech InfoTrellis is the Data and Analytics unit of Mastech Digital. At Mastech InfoTrellis, we have built intelligent Data Modernization practices and solutions to help companies harness the true potential of their data. Our expertise lies in providing timely insights from your data to make better decisions…FASTER. With our proven strategies and cutting-edge technologies, we foster intelligent decision-making, increase operational efficiency, and impact substantial business growth. With an unwavering commitment to building a better future, we are driven by the purpose of transforming businesses through data-powered innovation. (Who We Are | Mastech InfoTrellis) Mastech Digital is an Equal Opportunity Employer - All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.

Job Title: Associate Software Engineer(Trainee) Location: Mohali (On-site) Company: Prologic Technologies Experience Required: 0-6 months Employment Type: Full-time (In Office) About the Role Prologic Technologies is hiring a passionate Python Developer with experience in Machine Learning & AI to build intelligent and scalable solutions for global clients. You’ll collaborate with data scientists and product teams to bring ML models into production, automate workflows, and deliver measurable impact using modern AI tools and frameworks. Key Responsibilities Development & Integration Design, develop, and deploy ML/AI models using Python Integrate AI solutions into web apps, APIs, and internal tools Work on NLP, recommendation systems, classification, and predictive models Data Handling Build pipelines for data collection, cleaning, and transformation. Work with structured and unstructured data (text, images, logs). Optimize performance for large-scale datasets Model Training & Optimization Fine-tune models using real-world data Track and improve model accuracy, latency, and efficiency. Use libraries like TensorFlow, PyTorch, Scikit-learn, Hugging Face AI Automation & Deployment Use tools like MLflow, FastAPI, Docker for model lifecycle management. Automate tasks with AI agents or LLMs (OpenAI, Langchain, etc.) Build reusable modules, scripts, and workflows Must-Have Skills Strong Python coding skills. Hands-on experience with ML/AI frameworks: TensorFlow, PyTorch, Scikit-learn. Experience with model deployment using APIs, Docker, or cloud. Familiarity with NLP, computer vision, or deep learning. Git, Jupyter, Pandas, NumPy, REST APIs Nice to Have Experience with OpenAI APIs, Hugging Face Transformers, or LangChain. Exposure to cloud platforms (AWS/GCP/Azure). Knowledge of MLOps or AutoML pipelines Soft Skills We Value Curiosity to learn and experiment with AI. Ownership mindset and attention to detail. Ability to work independently and in teams. Clear communication and documentation habits Ready to Build the Future with AI? Email your resume and a brief write-up (max 300 words) on: A machine learning model you’ve built and deployed. Tools or techniques you used for optimization or automation Job Type: Full-time Schedule: Day shift Work Location: In person

Education: Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, or a related certifications or experience in NLP & CV. Years of Experience: Minimum 3 years of experience in Deep Learning, NLP,CV, MLOps and its related technologies. Responsibilities: - Design, develop, and deploy state-of-the-art NLP and CV models and algorithm - Collaborate with cross-functional teams to understand requirements and develop customised NLP & CV solutions and have experience in building the python backend using Flask / Django. - Database integration preferably using MongoDB or any other vector databases. - Maintain and improve the performance, accuracy, and efficiency of existing AI/ML models and their deployment on Cloud platforms (AWS) and monitor their performance using MLOps tools such as MLFlow, DVC. - Experience in building End to End data pipelines - Stay updated with emerging AI/ML technologies, LLM’s ,RAG - Conduct regular performance evaluations of AI/ML models using production grade MLOps solutions. - Troubleshoot and resolve any issues arising from the implementation of NLP & CV models. - Develop and monitor the code that runs in production environments using MLOps practices. Requirements: - Strong experience with Deep learning and NLP frameworks such as TensorFlow or other open-source machine learning frameworks. - Proficient in programming languages, such as Python or Java, and experience with AI/ML libraries. - Familiarity with the integration of APIs, such as REST API, OpenAI API, for implementing advanced AI-driven features. - Solid understanding of Machine learning and Deep learning algorithms, concepts, and best practices in a production environment using MLOps. - Experience with big data technologies, such as Hadoop and Spark, is a plus. - Strong problem-solving skills. - Excellent communication and teamwork skills, with the ability to collaborate effectively with team members from various disciplines. - Eagerness to learn and adapt to new technologies and industry trends. Job Types: Full-time, Permanent Pay: ₹30,000.00 - ₹50,000.00 per month Schedule: Day shift Application Question(s): Have you developed a chatbot? Are you ready to relocate to Coimbatore? Experience: AI: 2 years (Required) Work Location: In person

Build and manage CI/CD pipelines for ML models. Deploy models to cloud/on-premise environments. Monitor model performance and automate retraining workflows. Implement model versioning and reproducibility. Collaborate with data scientists and engineers. Requirements Looking for an ML DevOps Engineer to streamline the deployment and monitoring of ML models. The role requires strong DevOps skills with knowledge of ML lifecycle management Experience with Docker, Kubernetes, Jenkins, or similar tools. Familiarity with ML platforms like MLflow, Kubeflow, or SageMaker. Strong scripting skills in Python and Shell. Knowledge of cloud platforms (AWS, Azure, GCP). Understanding of MLOps best practices and ML lifecycle Date Opened 07/09/2025 Job Type Full time Years of Experience 3 - 6 Years Domain Chemicals City Chennai State/Province Tamil Nadu Country India Zip/Postal Code 600001

Responsibilities Design and implement scalable machine learning models from data preprocessing to deployment. Lead feature engineering and model optimization to improve performance and accuracy. Build and manage end-to-end ML pipelines using MLOps practices. Deploy, monitor, and maintain models in production environments. Collaborate with data scientists and product teams to understand business requirements and translate them into ML solutions. Conduct advanced data analysis and build visualization dashboards to support insights. Maintain thorough documentation of models, experiments, and workflows. Mentor junior team members on best practices and technical skills. Skills Must-have 2–3 years of experience in machine learning development, focusing on end-to-end model lifecycle. Proficiency in Python with Pandas, NumPy, and Scikit-learn for advanced data handling and feature engineering. Strong hands-on expertise in TensorFlow or PyTorch for deep learning model development. Good-to-have Experience with MLOps tools such as MLflow or Kubeflow for model management and deployment. Familiarity with big data frameworks like Spark or Dask. Exposure to cloud ML services such as AWS SageMaker or GCP AI Platform. Will be a plus Working knowledge of Weights & Biases and DVC for experiment tracking and versioning. Experience using Ray or BentoML for distributed training and model serving.

:-Job Title: Machine Learning Engineer – 2 Location: Onsite – Bengaluru, Karnataka, India Experience Required: 3 – 6 Years Compensation: ₹20 – ₹25 LPA Employment Type: Full-Time Work Mode: Onsite Only (No Remote) About the Company:- A fast-growing Y Combinator-backed SaaS startup is revolutionizing underwriting in the insurance space through AI and Generative AI. Their platform empowers insurance carriers in the U.S. to make faster, more accurate decisions by automating key processes and enhancing risk assessment. As they expand their AI capabilities, they’re seeking a Machine Learning Engineer – 2 to build scalable ML solutions using NLP, Computer Vision, and LLM technologies. Role Overview:- As a Machine Learning Engineer – 2, you'll take ownership of designing, developing, and deploying ML systems that power critical features across the platform. You'll lead end-to-end ML workflows, working with cross-functional teams to deliver real-world AI solutions that directly impact business outcomes. Key Responsibilities:- Design and develop robust AI product features aligned with user and business needs Maintain and enhance existing ML/AI systems Build and manage ML pipelines for training, deployment, monitoring, and experimentation Deploy scalable inference APIs and conduct A/B testing Optimize GPU architectures and fine-tune transformer/LLM models Build and deploy LLM applications tailored to real-world use cases Implement DevOps/ML Ops best practices with tools like Docker and Kubernetes Tech Stack & Tools Machine Learning & LLMs GPT, LLaMA, Gemini, Claude, Hugging Face Transformers PyTorch, TensorFlow, Scikit-learn LLMOps & MLOps Langchain, LangGraph, LangFlow, Langfuse MLFlow, SageMaker, LlamaIndex, AWS Bedrock, Azure AI Cloud & Infrastructure AWS, Azure Kubernetes, Docker Databases MongoDB, PostgreSQL, Pinecone, ChromaDB Languages Python, SQL, JavaScript What You’ll Do Collaborate with product, research, and engineering teams to build scalable AI solutions Implement advanced NLP and Generative AI models (e.g., RAG, Transformers) Monitor and optimize model performance and deployment pipelines Build efficient, scalable data and feature pipelines Stay updated on industry trends and contribute to internal innovation Present key insights and ML solutions to technical and business stakeholders Requirements Must-Have:- 3–6 years of experience in Machine Learning and software/data engineering Master’s degree (or equivalent) in ML, AI, or related technical fields Strong hands-on experience with Python, PyTorch/TensorFlow, and Scikit-learn Familiarity with ML Ops, model deployment, and production pipelines Experience working with LLMs and modern NLP techniques Ability to work collaboratively in a fast-paced, product-driven environment Strong problem-solving and communication skills Bonus Certifications such as: AWS Machine Learning Specialty AWS Solution Architect – Professional Azure Solutions Architect Expert Why Apply Work directly with a high-caliber founding team Help shape the future of AI in the insurance space Gain ownership and visibility in a product-focused engineering role Opportunity to innovate with state-of-the-art AI/LLM tech Be part of a fast-moving team with real market traction 📍 Note: This is an onsite-only role based in Bengaluru. Remote work is not available. Skills: software/data engineering,machine learning,ml, ai,sql,computer vision,tensorflow,ml ops,nlp,kubernetes,mongodb,llms and modern nlp techniques,python,postgresql,docker,azure,scikit-learn,python, pytorch/tensorflow, and scikit-learn,javascript,aws,llm technologies,pytorch

Overview As a key member of the team, you will be responsible for building and maintaining the infrastructure, tools, and workflows that enable the efficient, reliable, and secure deployment of LLMs in production environments. You will collaborate closely with data scientists, Data Engineers and product teams to ensure seamless integration of AI capabilities into our core systems. Responsibilities Design and implement scalable model deployment pipelines for LLMs, ensuring high availability and low latency. Build and maintain CI/CD workflows for model training, evaluation, and release. Monitor and optimize model performance, drift, and resource utilization in production. Manage cloud infrastructure (e.g., AWS, GCP, Azure) and container orchestration (e.g., Kubernetes, Docker) for AI workloads. Implement observability tools to track system health, token usage, and user feedback loops. Ensure security, compliance, and governance of AI systems, including access control and audit logging. Collaborate with cross-functional teams to align infrastructure with product goals and user needs. Stay current with the latest in MLOps and GenAI tooling and drive continuous improvement in deployment practices. Define and evolve the architecture for GenAI systems, ensuring alignment with business goals and scalability requirements Qualifications Bachelor’s or master’s degree in computer science, Software Engineering, Data Science, or a related technical field. 5 to 7 years of experience in software engineering, DevOps, and 3+ years in machine learning infrastructure roles. Hands-on experience deploying and maintaining machine learning models in production, ideally including LLMs or other deep learning models. Proven experience with cloud platforms (AWS, GCP, Azure) and container orchestration (Docker, Kubernetes). Strong programming skills in Python, with experience in ML libraries (e.g., TensorFlow, PyTorch, Hugging Face). Proficiency in CI/CD pipelines for ML workflows Experience with MLOps tools: MLflow, Kubeflow, DVC, Airflow, Weights & Biases. Knowledge of monitoring and observability tools

Designation: AI ML Engineer – Generative AI Solutions Mode: Hybrid Experience: 4+ years Location : Mumbai Salary : Upto 30LPA We hiring for one of our top-tier client organization , seeking a Senior AI ML Engineer with deep expertise in Generative AI, LLMs, OCR, RAG, and Azure-based deployments . If you're excited about working on cutting-edge AI systems that integrate LLMs (GPT, BERT, T5, LLaMA, Mistral) with real-world use cases across text, vision, and document AI , this role is for you. What You’ll Be Working On: Building RAG-based solutions , optimizing with prompt engineering , and deploying multi-modal AI (text + vision) Working on OCR-based AI for document understanding & extraction Designing and testing AI models using frameworks like LangChain, LlamaIndex, Hugging Face Deploying scalable solutions on Azure (OpenAI, ML, Cognitive Services) with CI/CD, Docker & Kubernetes Collaborating across DevOps, Product, and Business to create intelligent systems Key Technologies: LLMs : GPT, BERT, LLaMA, T5, Mistral Frameworks : LangChain, LlamaIndex, Hugging Face, OpenAI, PyTorch, TensorFlow Cloud & DevOps : Azure AI Services, Azure ML, Azure DevOps, Docker, Kubernetes Vector DBs : FAISS, Pinecone, ChromaDB Testing & MLOps : MLflow, Weights & Biases, custom QA sampling, BLEU, ROUGE Ideal Experience: 5+ years in AI/ML development with a minimum of 2 years in Generative AI Proven track record in deploying RAG and LLM-based solutions Deep understanding of Transformer models & OCR Hands-on with Azure AI stack ; bonus if you’ve dabbled in AWS/GCP