327 Aws Sagemaker Jobs

We are looking for: Lead Software Engineer Python , Youll make an impact by: Lead the design and development of Python-based software components for AI-driven systems. Define and uphold coding standards, best practices, and architectural principles (OOP, SOLID, design patterns). Collaborate with AI/ML engineers to integrate and productionize Machine Learning, Deep Learning, and Generative AI models. Architect and develop scalable RESTful APIs and backend systems using FastAPI or equivalent frameworks. Advocate for performance optimization, testability, and non-functional requirements (NFRs) across all solutions. Champion CI/CD practices, observability (logging, metrics, tracing), and maintain system reliability at scale. Mentor junior engineers and create a culture of high-quality, maintainable software development. Contribute to solution design for RAG (Retrieval-Augmented Generation) and Agentic AI workflows. Collaborate with multi-disciplinary teams to align software solutions with AI and business goals. Use your skills to move the world forward! Bachelors or Masters degree in Computer Science, Engineering, or a related field. 8+ years of hands-on experience in backend software development, with significant expertise in Python. Proven experience in leading and mentoring software development teams. Strong command over software architecture, design patterns, and clean coding principles. Experience in building and scaling API-based systems, preferably with FastAPI or similar frameworks. Solid understanding of integrating ML/DL/GenAI models into production applications. Familiarity with RAG, single and multi-agent architectures, and AI solution patterns. Practical experience with AWS (Sagemaker, Bedrock) and/or Azure (ML Studio, OpenAIq Service). Exposure to MLOps, model versioning, and observability tools. Working knowledge of Java or Rust is a plus. Experience designing software systems for cloud-native environments. Prior experience working in the Power and Energy domain. Familiarity with scalable data processing, real-time systems, or event-driven architectures. Exposure to open-source tools and frameworks in the GenAI ecosystem (e.g., LangGraph, LlamaIndex, SmolAgents). Deep commitment to quality, performance, and engineering excellence.

We are looking for: Graduate Trainee Engineer Youll make an impact by: Design, develop, and optimize NLP-driven AI solutions using state-of-the-art models and techniques (NER, embeddings, summarization, etc.). Build and productionize RAG pipelines and agentic workflows to support intelligent, context aware applications. Fine-tune, prompt-engineer, and deploy LLMs (OpenAI, Anthropic, Falcon, LLaMA, etc.) for domain-specific use cases. Collaborate with data scientists, backend developers, and cloud architects to build scalable AI first systems. Evaluate and integrate third-party models/APIs and open-source libraries for generative use cases. Continuously monitor and improve model performance, latency, and accuracy in production settings. Implement observability, performance monitoring, and explainability features in deployed models. Ensure solutions meet enterprise-level requirements for reliability, traceability, and maintainability. Use your skills to move the world forward! Masters or Bachelors degree in Computer Science, Machine Learning, AI, or a related field. Exposure in AI/ML, with knowlegde in NLP and Generative AI. Strong understanding of LLM architectures, fine-tuning methods (LoRA, PEFT), embeddings, and vector search. Experience in designing and deploying RAG pipelines and working with multi-step agent architectures. Proficiency in Python and frameworks like Lang Chain, Transformers (Hugging Face), Llama Index, Smol Agents, etc. Familiarity with ML observability and explainability tools (e.g., Tru Era, Arize, Why Labs). Knowledge of cloud-based ML services like AWS Sagemaker, AWS Bedrock, Azure OpenAI Service, Azure ML Studio, and Azure AI Foundry. Experience in integrating LLM-based agents in production environments. Understanding of real-time NLP challenges (streaming, latency optimization, multi-turn dialogues). Familiarity with Lang Graph, function calling, and tools for orchestration in agent-based systems. Exposure to infrastructure-as-code (Terraform/CDK) and DevOps for AI pipelines. Domain knowledge in Electrification, Energy, or Industrial AI is a strong plus..

We are looking for a highly skilled Machine Learning Engineer with strong expertise in SQL, Python, data modeling, and machine learning, along with hands-on experience working with Snowflake and AWS services like SageMaker. The ideal candidate will also be proficient in data visualization tools and familiar with big data frameworks. Key Responsibilities: Design, build, and deploy scalable machine learning models using Python and SQL. Work extensively with Snowflakes advanced features for data storage, processing, and ML integrations. Utilize AWS tools, especially SageMaker, for model training, deployment, and monitoring. Develop robust data pipelines and workflows, ensuring accuracy and efficiency. Create dashboards and visualizations to communicate data-driven insights. Collaborate with cross-functional teams including data engineers, product managers, and business analysts. Requirements: Proficiency in SQL, Python, data modeling, and machine learning algorithms. Hands-on experience with Snowflake and AWS SageMaker (or similar cloud-based ML services). Experience with data visualization tools like Tableau, Power BI, or equivalent. Familiarity with big data technologies such as Spark, Hadoop, or Kafka is a plus. Experience with other ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) is welcome. Strong problem-solving skills and the ability to work independently in a remote environment.

Design, develop, deploy ML models for real-world applications Implement, optimize ML pipelines using PySpark & MLflow Structured/ unstructured data using Pandas, NumPy Train models using scikit-learn, TensorFlow, PyTorch. Integrate LLMs Required Candidate profile Strong in Python, ML algorithms, model development, evaluation techniques Exp in PySpark model lifecycle management, MLflow Pandas, NumPy, and Scikit-learn, LLMs (OpenAI, Hugging Face Transformers)

Dear Job Seekers..! Greetings from Chimera Technologies..! We are looking for Data Scientist - 3-6 yrs experience - immediate joinee - willing to have 3-6 Months C2H mode . Kindly find the JD and JS as below Job Description (JD) Data Scientist Position Title: Data Scientist Experience Required: 56 Years Location: Bangalore Employment Type: C2H - 3-6 Months Joining: Immediate Role Overview: We are looking for a highly skilled and motivated Data Scientist with 56 years of experience to join our team immediately. The ideal candidate will have strong expertise in Python-based data science frameworks, machine learning, deep learning fundamentals, and cloud-based ML services. You will be responsible for building predictive models, performing data analysis, and delivering actionable insights through advanced analytics and visualization tools. Key Responsibilities: Develop and deploy machine learning models using Python frameworks such as Scikit-learn , XGBoost , and Scipy . Perform data manipulation and analysis using Pandas , NumPy , and Polars . Apply deep learning techniques (ANN, LSTM, etc.) for solving complex problems. Conduct model evaluation, tuning, and performance optimization. Create compelling visualizations using Matplotlib , Seaborn , Plotly , and BI tools like Tableau or Power BI . Work with cloud-based ML services such as AWS SageMaker , GCP BigQuery , or Azure ML . Write efficient SQL queries for data extraction and transformation. Collaborate with cross-functional teams to integrate data science solutions into business processes. (Optional) Work with ETL pipelines, Big Data tools like PySpark and Hadoop . Required Skills: Strong proficiency in Python ; knowledge of R is a plus. Solid understanding of SQL and data querying. Experience with Python libraries: Pandas , NumPy , Polars , Scikit-learn , XGBoost , Scipy . Knowledge of machine learning algorithms and model lifecycle. Basic understanding of deep learning architectures (ANN, LSTM). Visualization expertise using Matplotlib , Seaborn , Plotly , and BI tools ( Tableau , Power BI ). Experience with at least one cloud ML platform: AWS , GCP , or Azure . Familiarity with ETL processes and Big Data technologies is a plus. Job Specification (JS) Data Scientist Criteria Details Education - Bachelors or Masters in Data Science, Computer Science, Statistics, or related field Experience - 5–6 years in Data Science or related roles Technical Skills - Python, SQL, Scikit-learn, XGBoost, Pandas, NumPy, Tableau, Power BI Cloud Platforms - AWS SageMaker, GCP BigQuery, Azure ML (any one) Additional Skills - ETL, PySpark, Hadoop (preferred but not mandatory) Soft Skills- Analytical thinking, Communication, Collaboration, Problem-solving Availability - Immediate Joiner Location Bangalore - Hybrid Mode Regards HR Team

Dear Candidate, One of our Top MNC Immediate Hiring for MLOps Solution Architect role In Hyderabad Location Job Title: Sr. MLOps Specialist AWS Experience: 15 To 20 Years Location : Hyderabad Work Model : Work from office (5 Days) Employment Type: Full Time Skills : MLops,AWS,Data Bricks Position overview: Should have AWS hand-on experience in Design & Implement MLOps Pipelines and has done 5+ implementations and then moved to a Solution Architect role. Solution Design & Architecture : Designing and governing technical solutions, aligned with enterprise standards, security policies, and scalability goals. Technology Evaluation & Decision-Making: Evaluate tools, frameworks & AWS platform Deployment & Implementation Oversight: Deployment strategies like Blue/Green, Shadow environments etc. You can also pitch it as a Director Of MLOps, someone who will be hands on designing the architecture and be responsible to set up the ML Practice. Required Skills and Qualifications 12+ years in software engineering, DevOps, or ML Engineering with a focus on cloud- based ML pipelines Strong experience with Amazon Web Services (AWS), especially: Amazon SageMaker (training, deployment, Pipelines, Model Monitor) S3, Lambda, Step Functions, CodePipeline, ECR, Cloudwatch Proficiency in Python, Bash, and scripting for automation Familiarity with CI/CD tools like Jenkins, GitHub Actions, CodeBuild, etc. Experience with Docker and container orchestration in AWS (e.g., ECS, EKS optional) Understanding of ML lifecycle, including feature engineering, training, deployment, and monitoring Experience with data versioning and model tracking tools (e.g., MLflow, DVC, SageMaker Model Registry) Excellent communication and collaboration skills If any one Interested please share your cv uday.g@kanarystaffing.com

Role & responsibilities Experience working on AWS Cloud is a must, Data Science, Machine Learning , LLP, Deployment Experience. Preferred candidate profile Machine learning engineer having minimum 7 years of relevant experience & Aws minimum 3 Years.

Advanced AI and GenAI for Problem Discovery, RCA and Action Recommendation : Lead the team of data scientists, GenAI developers and ML engineers in hands-on mode. The team is engaged in developing and deploying advanced LLM, SLM and RAG based models along with classical machine learning models including anomaly detection, predictive analytics, causal inferencing and action recommendation models, to develop pipelines that autonomously discover problems, identify root causes and recommend actions in real-time and batch processing scenarios while ensuring that models are interpretable, scalable, and optimized for performance in production environments at big data scale. Gen-AI-Powered Analysis and Insights Generation: Lead the data scientists in hands-on mode creating and fine-tuning Generative AI models to assist in analysis creation for problem discovery, translating complex data patterns and root cause findings into actionable, natural language insights for business stakeholders. Implement prompt engineering and fine-tuning techniques to enhance the relevance and accuracy of insights. Reference Architectures : Work with Technical Architects to evolve frameworks for augmenting and evolving batch processing data architectures with streaming architectures to support connected data and ML pipeline executions in real-time. Adapt reference architectures that operationalize AI and Gen-AI models optimally in both batch and real-time modes. Client Deployments of the Platform: Consult and provide guidance for creating automated data pipelines for raw data and engineered features ensuring data quality, integrity, and accessibility for model training and inference. Help establish robust MLOps and LLMOps practices, including automated model monitoring, retraining triggers, and performance benchmarking including assessment of model accuracy and drift parameters and the business impact. Guide implementation of improvements based on feedback and new data. Development of Use Cases: Lead and support development of the use cases on the platform for various vertical specific problem statements. Support platform demonstration meetings by participating and leading discussions around explanation of platform capabilities and fitments. Leadership and Collaboration: Lead and mentor a team of data scientists and machine learning engineers, providing guidance on best practices in GenAI, AI/ML methodologies and coding standards. Collaborate closely with product managers, data engineers, and business analysts to align AI solutions with platform goals and user requirements in accordance with the platform roadmap. Pre-sales: Support pre-sales, marketing hyper-scaler partnerships and other sales activities such as RFPs by providing subject matter expertise during scoping and designing of the engagements. Support the client development efforts by contributing to developing proposals, POVs and other POC read-outs. Support pilot deployments at clients and strive for conversion into full programs. Technical Skills In-depth conceptual understanding of Statistics, Classical Machine Learning, Deep Learning and GenAI along with significant hands-on development experience in these areas Able to understand the nuances of business problems in various domains, quickly grasp the problem discovery analysis and modelling imperatives and translate those into the requirements for AI models Significant exposure to various proprietary & open-source cloud-based Machine Learning platforms such as Amazon Sagemaker / Azure Machine Learning Studio / Google Datalab ML Engine AutoML / H2O etc. Experience in leveraging open-source LLMs and prompt engineering, RAG based training and SLM model development Hands-on with excellent ML programming skills in Python using open-source libraries Experience in working with RDBMS and NoSQL databases; Exposure to Big Data technologies 14-16 years of Machine Learning model development and application experience Experience in building cloud-based products that implement GenAI and ML modelling as a service

Looking to make an impact in AI for telecom? As an AI Architect, youll drive the design and deployment of innovative AI/ML frameworks and solutions tailored for the telecom industry. You'll define architecture blueprints, choose the right tools and platforms, and partner with cross-functional teams to build scalable, high-impact AI systems. If you're passionate about driving real-world transformation through AI, this role is for you. You have: Deep understanding of AI/ML techniques and scalable architecture design. Experience architecting AI solutions for telecom operations, inventory management and ETL Strong experience with data platforms (Kafka, Spark, Hadoop), model orchestration (Kubeflow, MLflow), and cloud-native deployment (AWS Sagemaker, Azure ML). Proficient in programming (Python, Java) and DevOps/MLOps best practices. Familiarization with any of the LLM models and LLM agent frameworks like LangChain / CrewAI / AutoGen that helps in building and scaling applications using GenAI It would be nice if you also had: Familiarity with telecom protocols, OSS/BSS platforms, 5G architecture, and NFV/SDN concepts. Excellent communication and stakeholder management skills. Design end-to-end AI architecture tailored to telecom services business functions (e.g., Service assurance, Orchestration and Fulfilment). Define data strategy and AI workflows including Inventory Model, ETL, model training, deployment, and monitoring. Evaluate and select AI platforms, tools, and frameworks suited for telecom-scale workloads for development and testing of Inventory services solutions Work closely with telecom network experts and Architects to align AI initiatives with business goals. Ensure scalability, performance, and security in AI systems across hybrid/multi-cloud environments. Mentor AI developers

We are looking for: Senior Software Developer Python , Youll make an impact by: Write high-quality, testable, and maintainable Python code using object-oriented programming (OOP), SOLID principles, and design patterns. Develop RESTful APIs and backend services for AI/ML model serving using FastAPI. Collaborate with AI/ML engineers to integrate and deploy Machine Learning, Deep Learning, and Generative AI models into production environments. Contribute to software architecture and design discussions to ensure scalable and efficient solutions. Implement CI/CD pipelines and adhere to DevOps best practices for reliable and repeatable deployments. Design for observability, incorporating structured logging, performance monitoring, and alerting mechanisms. Optimize code and system performance, ensuring reliability and robustness at scale. Participate in code reviews, promote clean code practices, and mentor junior developers when needed. Use your skills to move the world forward! Bachelors or Masters degree in Computer Science, IT, or a related field. 5+ years of hands-on experience in software development, with a focus on Python. Deep understanding of OOP concepts, software architecture, and design patterns. Experience with backend web frameworks, preferably FastAPI. Familiarity with integrating ML/DL models into software solutions. Practical experience with CI/CD, containerization (Docker), and version control systems (Git). Exposure to MLOps practices and tools for model deployment and monitoring. Strong collaboration and communication skills in cross-functional engineering teams. Familiarity with cloud platforms like AWS (e.g., Sagemaker, Bedrock) or Azure (e.g., ML Studio, OpenAI Service). Experience in Rust is a strong plus. Experience working on high-performance, scalable backend systems. Exposure to logging/monitoring stacks like Prometheus, Grafana, ELK, or Open Telemetry. Understanding of data engineering concepts, ETL pipelines, and processing large datasets. Background or interest in the Power and Energy domain is a plus.

Role & responsibilities Design, develop, and deploy ML models on AWS SageMaker. Build and maintain scalable ML pipelines (training, testing, deployment, and monitoring). Collaborate with data engineers to prepare and preprocess large datasets. Optimize model performance, cost, and scalability in production environments. Implement MLOps best practices for CI/CD, monitoring, and retraining. Ensure compliance with data governance, security, and cloud standards. Qualifications: Bachelors/Masters in Computer Science, Data Science, or related field. 3+ years of experience in ML model development and deployment. Strong proficiency in Python, ML frameworks (TensorFlow, PyTorch, Scikit-learn). Proven expertise with AWS SageMaker and related AWS services (S3, Lambda, Step Functions, ECR, CloudWatch). Experience with containerization (Docker) and version control (Git). Familiarity with MLOps practices and ML lifecycle management. Preferred candidate profile We are seeking a skilled Machine Learning Engineer with hands-on experience in building, deploying, and optimizing ML models using AWS SageMaker. The ideal candidate will work closely with data scientists and engineering teams to operationalize machine learning solutions at scale.

Designation: Lead AI Engineer Company: Decorpot Location: Kolkata Experience Required: 5+ Years About Us: Decorpot is one of Indias leading interior design companies, known for bringing creativity, innovation, and cutting-edge technology into home interiors. As we expand into tech-driven solutions, we are looking for a passionate and experienced Lead AI Engineer to spearhead our AI initiatives, blending Artificial Intelligence with 3D visualization and digital experiences. Role Overview: As a Lead – AI Engineer , you will play a pivotal role in designing, developing, and deploying AI-powered solutions that enhance customer experience, streamline operations, and integrate advanced 3D visualization into our platforms. You will work closely with cross-functional teams, lead a small AI engineering squad, and drive innovation in the intersection of AI, 3D rendering, and cloud technologies. Key Responsibilities: Lead the design, development, and deployment of AI models and frameworks for real-world applications in the interior design domain. Work on 3D integrations using Three.js, Babylon.js, and WebGL for creating immersive visualization experiences. Develop, fine-tune, and deploy NLP, Computer Vision, and Recommendation Systems using TensorFlow, PyTorch, TensorFlow.js, and Hugging Face . Architect and manage scalable solutions on AWS SageMaker, S3, Google Cloud AI, or Azure ML . Ensure seamless integration of AI solutions with frontend and backend systems. Manage version control and CI/CD pipelines using GitHub/GitLab . Collaborate with product managers, designers, and developers using Agile workflows (JIRA, Trello, Slack) . Conduct API and web app testing using Postman and Selenium to ensure reliability and accuracy. Mentor and guide junior engineers, fostering a strong AI-first culture within the organization. Required Skills & Qualifications: Bachelor’s/Master’s degree in Computer Science, AI, Data Science, or a related field. 5+ years of professional experience in AI/ML engineering with leadership exposure. Strong hands-on experience with AI frameworks : TensorFlow, PyTorch, TensorFlow.js, Hugging Face. Proficiency in 3D technologies : Three.js, Babylon.js, WebGL. Expertise in cloud-based AI platforms : AWS, GCP, or Azure ML. Familiarity with Agile collaboration tools : JIRA, Trello, Slack. Solid understanding of API testing (Postman) and web app testing (Selenium) . Strong coding skills in Python, JavaScript, or related programming languages. Ability to lead a team, handle multiple projects, and deliver high-quality AI-driven solutions. What We Offer: Opportunity to lead AI innovations in the interior design and home dcor industry. Exposure to cutting-edge AI and 3D visualization technologies. Collaborative and dynamic work environment. Competitive compensation and growth opportunities.

Responsibilities Design, develop, and deploy end-to-end machine learning pipelines in cloud-native environments, ensuring scalability and reliability. Collaborate with data scientists to productionalize ML models, transitioning from prototype to enterprise-ready solutions. Optimize cloud-based data architectures and ML systems for high performance and cost efficiency (AWS, Azure, GCP). Integrate ML models into existing and new system architectures, designing robust APIs for seamless model consumption. Implement MLOps and LLMOps best practices, including CI/CD for ML models, monitoring, and automated retraining workflows. Continuously assess and improve ML system performance, ensuring high availability and minimal downtime. Stay ahead of AI and cloud trends, collaborating with cloud architects to leverage cutting-edge technologies Qualifications 4+ years of experience in cloud-native ML engineering, deploying and maintaining AI models at scale. Hands-on experience with AI cloud platforms (Azure ML, Google AI Platform, AWS SageMaker) and cloud-native services. Strong programming skills in Python and SQL, with expertise in cloud-native tools like Kubernetes and Docker. Experience building automated ML pipelines, including data preprocessing, model deployment, and monitoring. Proficiency in Linux environments and cloud infrastructure management. Experience operationalizing GenAI applications or AI assistants is a plus. Strong problem-solving, organizational, and communication skills.

Technologies: Amazon Bedrock, RAG Models, Java, Python, C or C++, AWS Lambda, Responsibilities: Responsible for developing, deploying, and maintaining a Retrieval Augmented Generation (RAG) model in Amazon Bedrock, our cloud-based platform for building and scaling generative AI applications. Design and implement a RAG model that can generate natural language responses, commands, and actions based on user queries and context, using the Anthropic Claude model as the backbone. Integrate the RAG model with Amazon Bedrock, our platform that offers a choice of high-performing foundation models from leading AI companies and Amazon via a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. Optimize the RAG model for performance, scalability, and reliability, using best practices and robust engineering methodologies. Design, test, and optimize prompts to improve performance, accuracy, and alignment of large language models across diverse use cases. Develop and maintain reusable prompt templates, chains, and libraries to support scalable and consistent GenAI applications. Skills/Qualifications: Experience in programming with at least one software language, such as Java, Python, or C/C++. Experience in working with generative AI tools, models, and frameworks, such as Anthropic, OpenAI, Hugging Face, TensorFlow, PyTorch, or Jupyter. Experience in working with RAG models or similar architectures, such as RAG, Ragna, or Pinecone. Experience in working with Amazon Bedrock or similar platforms, such as AWS Lambda, Amazon SageMaker, or Amazon Comprehend. Ability to design, iterate, and optimize prompts for various LLM use cases (e.g., summarization, classification, translation, Q&A, and agent workflows). Deep understanding of prompt engineering techniques (zero-shot, few-shot, chain-of-thought, etc.) and their effect on model behavior. Familiarity with prompt evaluation strategies, including manual review, automatic metrics, and A/B testing frameworks. Experience building prompt libraries, reusable templates, and structured prompt workflows for scalable GenAI applications. Ability to debug and refine prompts to improve accuracy, safety, and alignment with business objectives. Awareness of prompt injection risks and experience implementing mitigation strategies. Familiarity with prompt tuning, parameter-efficient fine-tuning (PEFT), and prompt chaining methods. Familiarity with continuous deployment and DevOps tools preferred. Experience with Git preferred Experience working in agile/scrum environments Successful track record interfacing and communicating effectively across cross-functional teams. Good communication, analytical and presentation skills, problem-solving skills and learning attitude

About EarnIn As one of the first pioneers of earned wage access, our passion at EarnIn is building products that deliver real-time financial flexibility for those with the unique needs of living paycheck to paycheck. Our community members access their earnings as they earn them, with options to spend, save, and grow their money without mandatory fees, interest rates, or credit checks. Were fortunate to have an incredibly experienced leadership team, combined with world-class funding partners like A16Z, Matrix Partners, DST, Ribbit Capital, and a very healthy core business with a tremendous runway. Were growing fast and are excited to continue bringing world-class talent onboard to help shape the next chapter of our growth journey. POSITION SUMMARY EarnIn is undergoing transformative investments in machine learning products, algorithms, and platforms. We seek highly skilled and experienced ML engineers to lead our algorithmic marketing initiatives from our Bengaluru office in India. You will apply machine learning models, statistical techniques, experiment design, and data analysis to optimize marketing spend on user acquisition and retention, ensuring the most efficient use of resources. You will catalyze our healthy and growing business, creating first-of-a-kind success stories for marketing through machine learning. This position will be a hybrid role from our Bengaluru office, as part of our expanding site location, with 2 days a week spentin the office. EarnIn provides excellent benefits for our employees, including healthcare, internet/cell phone reimbursement, a learning and development stipend, and potential opportunities to travel to our Palo Alto HQ. Our salary ranges are determined by role, level, and location. WHAT YOU&aposLL DO Design, build, A/B test, and maintain machine learning and statistical models to optimize audience, channel, campaign, creative, and customer journey, improving ROI for user acquisition and lifecycle marketing. Continuously optimize the quality of our machine learning models for Marketing Mix Modeling (MMM), predicted Lifetime Value (LTV), incremental Lift estimation, and causal inference. Analyze large datasets to extract actionable insights and inform marketing decisions. Collaborate with multiple teams in the US and India. Communicate findings and recommendations to stakeholders clearly and concisely. Champion scientific rigor in data-driven decision-making. Stay up-to-date with the latest advancements in machine learning, statistical techniques, and marketing analytics to recommend newer business opportunities WHAT WE&aposRE LOOKING FOR Bachelor&aposs or Masters degree in Computer Science, Engineering, or a related field. 3+ years of industry machine learning experience and excellent software engineering skills. Familiarity with digital marketing concepts and metrics. Prior experience with Marketing Mix Modeling (MMM), predicted Lifetime Value (LTV), incremental Lift estimation, geo lift tests, and causal inference is a big plus. Expertise in the breadth of machine learning and statistical techniques such as tree-based models, deep learning techniques, and A/B testing. Strong programming skills in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, XGBoost, and Scikit-Learn. Hands-on experience with SQL and data manipulation tools. Experience with ML cloud platforms such as AWS Sagemaker, Databricks, or GCP Vertex AI Strong analytical skills to evaluate business impact and performance metrics of ML implementations and drive continuous improvement Strong communication and collaboration skills. Passion for learning and staying updated with the latest machine learning industry trends. At EarnIn, we believe that the best way to build a financial system that works for everyday people is by hiring a team that represents our diverse community. Our team is diverse not only in background and experience but also in perspective. We celebrate our diversity and strive to create a culture of belonging. EarnIn does not unlawfully discriminate based on race, color, religion, sex (including pregnancy, childbirth, breastfeeding, or related medical conditions), gender identity, gender expression, national origin, ancestry, citizenship, age, physical or mental disability, legally protected medical condition, family care status, military or veteran status, marital status, registered domestic partner status, sexual orientation, genetic information, or any other basis protected by local, state, or federal laws. EarnIn is an E-Verify participant. EarnIn does not accept unsolicited resumes from individual recruiters or third-party recruiting agencies in response to job postings. No fee will be paid to third parties who submit unsolicited candidates directly to our hiring managers or HR team. Show more Show less

Role & responsibilities: Outline the day-to-day responsibilities for this role. Preferred candidate profile: Specify required role expertise, previous job experience, or relevant certifications. Perks and benefits: Mention available facilities and benefits the company is offering with this job.

Role Summary : We are seeking a highly skilled Senior Data Science Consultant with 8+ years of experience to lead an internal optimization initiative. The ideal candidate should have a strong background in data science, operations research, and mathematical optimization, with a proven track record of applying these skills to solve complex business problems. This role requires a blend of technical depth, business acumen, and collaborative communication. A background in internal efficiency/operations improvement or cost/resource optimization projects is highly desirable. Key Responsibilities : - Lead and contribute to internal optimization-focused data science projects from design to deployment. - Develop and implement mathematical models to optimize resource allocation, process performance, and decision-making. - Use techniques such as linear programming, mixed-integer programming, heuristic and metaheuristic algorithms. - Collaborate with business stakeholders to gather requirements and translate them into data science use cases. - Build robust data pipelines and use statistical and machine learning methods to drive insights. - Communicate complex technical findings in a clear, concise manner to both technical and non-technical audiences. - Mentor junior team members and contribute to knowledge sharing and best practices within the team. Required Skills And Qualifications : - Masters or PhD in Data Science, Computer Science, Operations Research, Applied Mathematics, or related fields. - Minimum 8 years of relevant experience in data science, with a strong focus on optimization. - Expertise in Python (NumPy, Pandas, SciPy, Scikit-learn), SQL, and optimization libraries such as PuLP, Pyomo, Gurobi, or CPLEX. - Experience with end-to-end lifecycle of internal optimization projects. - Strong analytical and problem-solving skills. - Excellent communication and stakeholder management abilities. Preferred Qualifications : - Experience working on internal company projects focused on logistics, resource planning, workforce optimization, or cost reduction. - Exposure to tools/platforms like Databricks, Azure ML, or AWS SageMaker. - Familiarity with dashboards and visualization tools like Power BI or Tableau. - Prior experience in consulting or internal centers of excellence (CoE) is a plus.

Responsibilities Design, develop, and deploy end-to-end machine learning pipelines in cloud-native environments, ensuring scalability and reliability. Collaborate with data scientists to productionalize ML models, transitioning from prototype to enterprise-ready solutions. Optimize cloud-based data architectures and ML systems for high performance and cost efficiency (AWS, Azure, GCP). Integrate ML models into existing and new system architectures, designing robust APIs for seamless model consumption. Implement MLOps and LLMOps best practices, including CI/CD for ML models, monitoring, and automated retraining workflows. Continuously assess and improve ML system performance, ensuring high availability and minimal downtime. Stay ahead of AI and cloud trends, collaborating with cloud architects to leverage cutting-edge technologies Qualifications 4+ years of experience in cloud-native ML engineering, deploying and maintaining AI models at scale. Hands-on experience with AI cloud platforms (Azure ML, Google AI Platform, AWS SageMaker) and cloud-native services. Strong programming skills in Python and SQL, with expertise in cloud-native tools like Kubernetes and Docker. Experience building automated ML pipelines, including data preprocessing, model deployment, and monitoring. Proficiency in Linux environments and cloud infrastructure management. Experience operationalizing GenAI applications or AI assistants is a plus. Strong problem-solving, organizational, and communication skills. Location: Bengaluru,Indore,Pune

AI Engineer (Onsite - Ahmedabad) Summary: We are looking for an experienced AI Engineer with 4 to 8 years of expertise in AI, machine learning, and LLM development . The ideal candidate will have hands-on experience in building, deploying, and optimizing AI models, along with strong problem-solving and collaboration skills. Key Responsibilities: Design, develop, and optimize AI/ML algorithms and models for real-world applications Integrate AI solutions into existing systems with cross-functional teams Research and apply the latest AI advancements Mentor junior engineers and participate in code reviews Ensure scalable, production-grade AI deployments Requirments: Strong programming skills (Python, Java, or C++) Proficiency with LangChain, LangGraph, vector DBs (Pinecone, ChromaDB, FAISS) Hands-on experience with Hugging Face, transformers, fine-tuning & deploying LLMs (LLaMA, Mistral, Falcon, OpenAI, etc.) Expertise in prompt engineering, RAG, and model serving (FastAPI, Hugging Face Inference Endpoints, AWS SageMaker) Experience with TensorFlow, PyTorch, or scikit-learn Knowledge of cloud platforms (AWS/Azure) Strong communication, teamwork, and problem-solving skills

About the Role: We are seeking a highly skilled and experienced Machine Learning Engineer to join our dynamic team. As a Machine Learning Engineer, you will be responsible for the design, development, deployment, and maintenance of machine learning models and systems that drive our [mention specific business area or product, e.g., recommendation engine, fraud detection system, autonomous vehicles]. You will work closely with data scientists, software engineers, and product managers to translate business needs into scalable and reliable machine learning solutions. This is a key role in shaping the future and requires a strong technical foundation combined with a passion for innovation and problem-solving. Responsibilities: Model Development & Deployment: Design, develop, and deploy machine learning models using various algorithms (e.g., regression, classification, clustering, deep learning) to solve complex business problems. Select appropriate datasets and features for model training, ensuring data quality and integrity. Implement and optimize model training pipelines, including data preprocessing, feature engineering, model selection, and hyperparameter tuning. Deploy models to production environments using containerization technologies (e.g.,Docker, Kubernetes) and cloud platforms (e.g., AWS, GCP, Azure). Monitor model performance in production, identify and troubleshoot issues, and implement model retraining and updates as needed. Infrastructure & Engineering: Develop and maintain APIs for model serving and integration with other systems. Write clean, well-documented, and testable code. Collaborate with software engineers to integrate models into existing products and services. Research & Innovation : Stay up to date with the latest advancements in machine learning and related technologies. Research and evaluate new algorithms, tools, and techniques to improve model performance and efficiency. Contribute to the development of new machine learning solutions and features. Proactively identify opportunities to leverage machine learning to solve business challenges. Collaboration & Communication: * Collaborate effectively with data scientists, software engineers, product managers, and other stakeholders. * Communicate technical concepts and findings clearly and concisely to both technical and non-technical audiences. * Participate in code reviews and contribute to the team's knowledge sharing. Qualifications: * Experience : 7+ years of experience in machine learning engineering or a related field. Technical Skills: Programming Languages : Proficient in Python and experience with other languages (e.g., Java, Scala, R) is a plus. Machine Learning Libraries : Strong experience with machine learning libraries and frameworks such as scikit-learn, TensorFlow, PyTorch, Keras, etc. Data Processing : Experience with data manipulation and processing using libraries like Pandas, NumPy, and Spark. Model Deployment : Experience with model deployment frameworks and platforms (e.g., TensorFlow Serving, TorchServe, Seldon, AWS SageMaker, Google AI Platform, Azure Machine Learning). Databases : Experience with relational and NoSQL databases (e.g., SQL, MongoDB, Cassandra). Version Control : Experience with Git and other version control systems. DevOps : Familiarity with DevOps practices and tools. Strong understanding of machine learning concepts and algorithms : Regression, Classification, Clustering, Deep Learning etc. Soft Skills: Excellent problem-solving and analytical skills. Strong communication and collaboration skills.

Job Title: Machine Learning Engineer Location: Jaipur Job Type: Full-Time About the Role We are looking for a Machine Learning Engineer with expertise in YOLO, custom ML models, and deep learning to join our team. If you have 4+ years of experience in developing and deploying computer vision solutions, this is an exciting opportunity to work on cutting-edge AI applications. Key Responsibilities Develop, train, fine-tune, and optimize YOLO-based object detection models (YOLOv5, YOLOv8, etc.). Design and implement custom ML models tailored to specific business needs. Handle large-scale datasets, including data collection, preprocessing, and augmentation. Deploy ML models in cloud environments (AWS, GCP, Azure) and edge computing platforms (Jetson Nano, Raspberry Pi, Coral TPU, etc.). Optimize ML models using techniques such as quantization, pruning, and model compression. Evaluate and improve model performance using mAP (mean Average Precision), IoU (Intersection over Union), precision-recall, and F1-score. Work closely with data scientists, software engineers, and DevOps teams to integrate models into production systems. Stay updated with the latest advancements in deep learning, computer vision, and AI model optimization. Required Skills & Qualifications Minimum 4+ years of experience in Machine Learning, Deep Learning, and Computer Vision. Strong expertise in YOLO-based object detection models (YOLOv5, YOLOv7, YOLOv8, etc.). Proficiency in Python, TensorFlow, PyTorch, and Keras. Experience with OpenCV, FastAPI, Flask for computer vision applications and API deployment. Knowledge of ONNX, TensorRT for model deployment and optimization. Familiarity with Docker, Kubernetes, MLflow, Kubeflow, and Airflow for MLOps and model lifecycle management. Experience working with cloud-based ML services (AWS SageMaker, Google Vertex AI, Azure ML). Strong understanding of CNNs, RNNs, Transformers, and other deep learning architectures. Hands-on experience in dataset annotation, synthetic data generation, and image preprocessing. Preferred Qualifications Experience in real-time AI applications, robotics, smart surveillance, or autonomous systems. Knowledge of Edge AI deployment on NVIDIA Jetson, Coral TPU, or Raspberry Pi. Experience with generative AI models, self-supervised learning, and synthetic data creation. Prior work in video analytics, smart security systems, healthcare AI, or industrial automation.

Responsibilities Design, develop, and deploy end-to-end machine learning pipelines in cloud-native environments, ensuring scalability and reliability. Collaborate with data scientists to productionalize ML models, transitioning from prototype to enterprise-ready solutions. Optimize cloud-based data architectures and ML systems for high performance and cost efficiency (AWS, Azure, GCP). Integrate ML models into existing and new system architectures, designing robust APIs for seamless model consumption. Implement MLOps and LLMOps best practices, including CI/CD for ML models, monitoring, and automated retraining workflows. Continuously assess and improve ML system performance, ensuring high availability and minimal downtime. Stay ahead of AI and cloud trends, collaborating with cloud architects to leverage cutting-edge technologies Qualifications 4+ years of experience in cloud-native ML engineering, deploying and maintaining AI models at scale. Hands-on experience with AI cloud platforms (Azure ML, Google AI Platform, AWS SageMaker) and cloud-native services. Strong programming skills in Python and SQL, with expertise in cloud-native tools like Kubernetes and Docker. Experience building automated ML pipelines, including data preprocessing, model deployment, and monitoring. Proficiency in Linux environments and cloud infrastructure management. Experience operationalizing GenAI applications or AI assistants is a plus. Strong problem-solving, organizational, and communication skills. Location: ,Indore,Pune

We're Nagarro. We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale across all devices and digital mediums, and our people exist everywhere in the world (17500+ experts across 39 countries, to be exact). Our work culture is dynamic and non-hierarchical. We're looking for great new colleagues. That's where you come in! REQUIREMENTS: Total experience 5+ years. Strong proficiency in Python for Data Science , with experience in libraries like Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch. Hands on working experience in Python. Experience with data visualization libraries (e.g., matplotlib, seaborn, plotly) Hands on working experience in AWS Sagemaker. Basic to intermediate SQL skills Experience working with GCP. Hands-on experience deploying ML solutions using Kubeflow, Vertex AI, Airflow, or PySpark Excellent communication and collaboration skills for working across global teams. RESPONSIBILITIES: Writing and reviewing great quality code. Understanding the clients business use cases and technical requirements and be able to convert them into technical design which elegantly meets the requirements. Mapping decisions with requirements and be able to translate the same to developers. Identifying different solutions and being able to narrow down the best option that meets the clients requirements. Defining guidelines and benchmarks for NFR considerations during project implementation Writing and reviewing design document explaining overall architecture, framework, and high-level design of the application for the developers Reviewing architecture and design on various aspects like extensibility, scalability, security, design patterns, user experience, NFRs, etc., and ensure that all relevant best practices are followed. Developing and designing the overall solution for defined functional and non-functional requirements; and defining technologies, patterns, and frameworks to materialize it Understanding and relating technology integration scenarios and applying these learnings in projects Resolving issues that are raised during code/review, through exhaustive systematic analysis of the root cause, and being able to justify the decision taken. Carrying out POCs to make sure that suggested design/technologies meet the requirements.

Responsibilities Design, develop, and deploy end-to-end machine learning pipelines in cloud-native environments, ensuring scalability and reliability. Collaborate with data scientists to productionalize ML models, transitioning from prototype to enterprise-ready solutions. Optimize cloud-based data architectures and ML systems for high performance and cost efficiency (AWS, Azure, GCP). Integrate ML models into existing and new system architectures, designing robust APIs for seamless model consumption. Implement MLOps and LLMOps best practices, including CI/CD for ML models, monitoring, and automated retraining workflows. Continuously assess and improve ML system performance, ensuring high availability and minimal downtime. Stay ahead of AI and cloud trends, collaborating with cloud architects to leverage cutting-edge technologies Qualifications 4+ years of experience in cloud-native ML engineering, deploying and maintaining AI models at scale. Hands-on experience with AI cloud platforms (Azure ML, Google AI Platform, AWS SageMaker) and cloud-native services. Strong programming skills in Python and SQL, with expertise in cloud-native tools like Kubernetes and Docker. Experience building automated ML pipelines, including data preprocessing, model deployment, and monitoring. Proficiency in Linux environments and cloud infrastructure management. Experience operationalizing GenAI applications or AI assistants is a plus. Strong problem-solving, organizational, and communication skills.

Responsibilities Design, develop, and deploy end-to-end machine learning pipelines in cloud-native environments, ensuring scalability and reliability. Collaborate with data scientists to productionalize ML models, transitioning from prototype to enterprise-ready solutions. Optimize cloud-based data architectures and ML systems for high performance and cost efficiency (AWS, Azure, GCP). Integrate ML models into existing and new system architectures, designing robust APIs for seamless model consumption. Implement MLOps and LLMOps best practices, including CI/CD for ML models, monitoring, and automated retraining workflows. Continuously assess and improve ML system performance, ensuring high availability and minimal downtime. Stay ahead of AI and cloud trends, collaborating with cloud architects to leverage cutting-edge technologies Qualifications 4+ years of experience in cloud-native ML engineering, deploying and maintaining AI models at scale. Hands-on experience with AI cloud platforms (Azure ML, Google AI Platform, AWS SageMaker) and cloud-native services. Strong programming skills in Python and SQL, with expertise in cloud-native tools like Kubernetes and Docker. Experience building automated ML pipelines, including data preprocessing, model deployment, and monitoring. Proficiency in Linux environments and cloud infrastructure management. Experience operationalizing GenAI applications or AI assistants is a plus. Strong problem-solving, organizational, and communication skills. Location: ,Indore,Pune