Top Machine Learning Jobs in India

Lead design, build and maintenance of data architectures, pipelines, warehouses and lakes; enable ML lifecycle by preparing governed datasets, collaborating with data scientists, and driving data governance, lineage, access controls and analytics-ready schema design.

Develop and own production machine learning and AI systems across their lifecycle in the food and beverage domain. Responsibilities include designing datasets, evaluation criteria, and test cases; monitoring model performance, cost, accuracy, latency, and reliability; leading experimentation and code design; collaborating with engineering, product, and sales; applying agentic coding practices; and partnering with AI product teams to improve customer experiences.

Lead development of generative AI solutions that enhance business processes, mentor teams, and innovate AI technologies in finance.

Lead and scale an AI engineering team to design, train, optimize, and deploy deep learning models for 2D/3D medical imaging. Oversee model architecture, MLOps pipelines, clinical integrations (PACS/RIS), and optimize models for cloud, edge, and clinical hardware while mentoring engineers and ensuring regulatory and data privacy considerations.

Design, build, and maintain a scalable Python-based BDD automation platform for desktop and web apps. Integrate LLMs (Azure OpenAI) and orchestration libraries (e.g., LangChain) to accelerate BDD step classification and test authoring. Develop data pipelines, ensure CI/CD integration (Jenkins), manage configuration consistency, mentor automation engineers, and improve deployment, observability, and reporting for enterprise-grade QA automation.

Design, develop, and deploy production-ready AI/ML solutions using LLMs and cloud AI services. Lead and mentor teams, make technical decisions, integrate generative models, and optimize ML systems including deep learning, chatbot, and image-processing components.

Lead design and delivery of large-scale AI/ML systems, microservices, and data engineering pipelines. Build and deploy LLM-based solutions using modern ML frameworks, cloud-native architectures, CI/CD, and observability while managing stakeholders and enterprise agile delivery in financial services.

Build and evaluate multi-agent AI systems, agentic coding tools, and statistical and machine learning models. Contribute to MLOps practices for deploying, monitoring, and maintaining production models. Analyze model performance through experiments and testing, and optimize or fine-tune existing models. The internship requires strong Python, Linux, SQL, Git, and Bash skills, plus experience with modern agentic AI tooling and machine learning frameworks.

Own the full machine learning development lifecycle by architecting, building, and deploying scalable production ML systems on AWS. Lead NLP and generative AI applications for sentiment analysis, moderation, recommendations, and search. Establish MLOps, CI/CD, monitoring, governance, and observability practices; resolve complex infrastructure challenges; mentor engineers; and collaborate with data science, product, and engineering teams.

Design and deploy production-ready AI/ML applications using LLMs, generative AI, cloud AI services, and on-premises pipelines. Lead AI model development, integrate solutions across business processes, mentor junior engineers, make team decisions, and collaborate cross-functionally. Work may involve deep learning, neural networks, chatbots, and image processing while ensuring alignment with project goals, timelines, and quality standards.

The role involves managing ML models, deploying them in production, evaluating technologies, and implementing CI/CD processes while supporting model development and ensuring data security.

Develop and deploy production-ready AI/ML applications using cloud AI services and SageMaker. Integrate generative AI models and work on deep learning, chatbots, and image processing. Lead and mentor team members, make technical decisions, and collaborate across teams to deliver robust AI solutions.

Build and operate an AI-powered root cause analysis engine using vector databases, embedding pipelines, RAG retrieval, prompt engineering, structured LLM outputs, confidence calibration, and evaluation frameworks. Develop ingestion workflows, tenant isolation controls, FastAPI validation and routing logic, golden test sets, retrieval-quality metrics, prompt versioning, and cost and latency monitoring for production incident analysis.

Lead end-to-end ML architecture, feature engineering, and production pipelines. Design/deploy CI/CD (Docker, Kubernetes, GitHub Actions), monitor production ML/LLM systems, build agentic AI using LangGraph, implement RAG with vector DBs like Pinecone, establish LLMOps practices, define APIs/contracts with partner teams, and provide technical oversight to junior engineers.

Architect and develop AI/ML compiler and runtime software for RISC-V, NPU, and SoC platforms. Build IREE and MLIR compiler pipelines, custom dialects, lowering passes, code generation, runtime integration, quantization, and accelerator support. Optimize edge AI workloads for latency, throughput, memory, and power. Lead validation, benchmarking, CI, silicon bring-up, debugging, and cross-functional hardware-software enablement while providing technical direction and mentoring.

Deploys and productionizes machine learning models for a global mobility platform. Responsibilities include building MLOps capabilities, managing model versioning and serving, developing inference pipelines, monitoring system performance, creating containerized environments, and integrating cloud-native and distributed technologies. The role also supports benchmarking, troubleshooting, optimization, and scalable deployment while collaborating with data scientists, cloud engineers, and data engineers.

Lead the rebuild of Roku's relevance stack using state-of-the-art ML (LLMs, transformers, deep learning, bandits) for search, ranking, and monetization. Run AB tests, analyze KPIs, collaborate cross-functionally, scale ML systems and data pipelines, and mentor/hire engineers.

Design, build, and productionize scalable ML and generative-AI capabilities for identity resolution, audience modeling, attribution, and optimization. Develop data pipelines, features, experiments, and monitoring; integrate LLM/Gen-AI features and partner with product, data science, and platform teams to drive advertiser outcomes.

Lead design and implementation of scalable AI/ML architectures and autonomous agent systems for debugging and power/performance optimization. Build analytics and ML models using telemetry to detect anomalies, predict performance, and deliver system-level optimizations while collaborating with hardware, software, and platform teams to deploy solutions.

Architect and implement enterprise-grade MLOps/LLMOps on AWS (EKS-first), building scalable pipelines for training, validation, registry, deployment, monitoring, CI/CD, observability, lineage and governance; serve as technical authority, conduct architecture reviews, troubleshoot systems, and mentor engineering teams across projects.

Develop and optimize ML models for computer vision tasks, build data processing pipelines, and collaborate on robotics integration.

Lead design, development, and productionalization of ML/GenAI applications including model evaluation, fine-tuning, prompt engineering, RAG pipelines, and scalable ML pipelines on cloud platforms. Mentor teams, collaborate on data preprocessing and feature engineering, and deliver reliable production ML systems involving vector search, inference serving, and monitoring.

Design, develop, and deploy AI/ML solutions from prototype to production, including traditional ML and modern LLM/generative/agentic systems. Build end-to-end pipelines, ensure model evaluation, fairness and reliability, collaborate with stakeholders, and meet healthcare regulatory and governance requirements.

Serve as a hands-on technical architect advising customers on designing, building, and deploying data science and ML workloads on Snowflake. Create POCs, implement ML pipelines using SQL and Python (or Java/Scala), apply MLOps best practices, collaborate with SIs, and enable customers through knowledge transfer and strategic guidance.

Provide senior-level technical expertise to debug, optimise, and productionize GenAI and ML workloads on Databricks. Troubleshoot data and ML pipelines, LLM deployments, model serving, vector search, agent systems, monitoring, and cost/latency issues. Advise customers on MLOps/LLMOps, experiment tracking, model lifecycle, prompt engineering, retrieval/embedding systems, and collaborate with engineering and account teams to influence product and create documentation.