Top Machine Learning Jobs in India

The Sr. Security Engineer will design advanced DLP solutions, implement AI data classification, and create protection standards for sensitive IP within the organization.

Manage verification for complex DRAM design projects, develop methodologies, lead teams, provide technical advice, and enhance AI-driven verification processes.

Design, build, and operationalize a unified AI/ML data collection platform and production LLM workflows (RAG, embeddings, vector DBs). Lead model lifecycle management, observability, scalable inference, data quality/evaluation, distributed cloud-native architectures, and mentor engineers to deliver production-ready, cost-efficient ML systems.

Build and scale a unified AI/ML data collection platform: design, develop, test, deploy, and monitor LLM-driven data pipelines, RAG/embedding workflows, model lifecycle management, observability, and production MLOps systems to ensure reliable, maintainable, and cost-efficient ML capabilities.

Design, implement, validate, and deploy machine learning models and data pipelines. Author technical documentation, run evaluations and case studies, design proofs of concept, and contribute to patents and APIs. Manage production model deployments and support research for future product development.

Build and deploy production-grade ML/LLM solutions for ad targeting, ranking, pacing, personalization, and creative intelligence. Design low-latency ML lifecycles, operate services at scale, integrate agentic LLM workflows, optimize inference, monitor model performance, and mentor engineers.

Lead design, development, and deployment of production ML systems for HD mapping, ADAS and IVI. Own end-to-end ML architecture, data ingestion, training, inference pipelines, MLOps (CI/CD, versioning, monitoring), and integrate outputs into map compilation. Drive models in computer vision, NLP, generative/agentic AI, and ensure scalable, reliable in-cloud or in-vehicle deployments while mentoring teams and enforcing engineering best practices.

Lead design, development, and deployment of scalable AI/ML and NLP solutions, including intelligent agents and prompt engineering. Mentor team members, integrate with MCP servers for large-scale AI computations, ensure ethical AI practices, collaborate cross-functionally, and translate research into production-ready capabilities while maintaining data privacy and security compliance.

Lead architecture, development, and deployment of enterprise-scale conversational AI (voice bots, chatbots, IVR) using Dialogflow CX and GCP. Train and optimize NLP/NLU models, implement CI/CD for AI/ML, mentor engineers, and collaborate with cloud architects and product owners to build secure, scalable conversational workflows.

As an ML Engineer, you will design, experiment, and deploy AI/ML models, evaluate their performance, and leverage Python and SQL.

As a Staff Machine Learning Engineer, you will lead the development of advanced ML systems, collaborate across teams, and drive AI solutions to enhance clinical workflows and automate decision-making.

As a Staff Machine Learning Engineer, you will design and deploy scalable ML systems, lead projects, mentor engineers, and implement MLOps practices, while collaborating with cross-functional teams.

Lead the Data and ML Platform team, defining vision and strategy, overseeing platform development, fostering innovation, and managing a distributed team.

Lead design, development, and operation of production AI/ML systems (LLMs, RAG, multi-agent). Drive architectures, MLOps, model evaluation, cloud deployments, and mentor junior engineers to align AI with product needs.

Lead hands-on experimentation and prototyping of Generative and Agentic AI solutions (LLMs, RAG, vector retrieval). Build production-ready POCs, reusable prompts, architectures, and implementation assets; enable engineering adoption; apply AI development lifecycle practices, evaluation, cost optimization, and governance; measure POC-to-production adoption and business impact.

Design, build, deploy, and support scalable production AI/ML solutions following AIDLC. Develop data pipelines, feature engineering, model training/evaluation, deployment, monitoring, and MLOps/LLMOps. Integrate Generative and Agentic AI (LLMs, RAG, embeddings, prompt engineering), collaborate with cross-functional teams, and enforce governance, security, and Responsible AI practices.

Lead design, experimentation, and delivery of Generative AI, Agentic AI, and ML solutions. Build production-ready POCs, reusable assets, evaluation frameworks, and collaborate with engineering and product teams to transition prototypes into scalable, secure production systems while applying AIDLC practices and responsible AI principles.

Design, develop, deploy, and maintain scalable ML solutions and pipelines. Perform EDA, feature engineering, model training, evaluation, deployment, monitoring, and retraining. Implement MLOps practices, observability, and production support. Integrate AI into applications, follow Responsible AI and governance, and explore emerging Generative AI/LLM capabilities to drive business outcomes.

Design, build, and optimize LLM-based features (chat, summarization, RAG) including prompt engineering, fine-tuning, embeddings, vector search, safety/evaluation, API integrations, and performance optimizations while collaborating with product and engineering teams.

Design, build, deploy, and operate voice and chat conversational AI solutions (Lex, Dialogflow, Google CX). Create intents, entities, multi-turn flows, and voice-first capabilities. Integrate with CCaaS platforms and backend APIs, manage model lifecycle, implement CI/CD and observability, and optimize for low latency and seamless human handoff.

The GIS Manager will lead geospatial initiatives for catastrophe risk modeling, managing teams, and overseeing project execution and data management.

Lead site-level process excellence and continuous improvement initiatives using LSS; drive customer experience and operational metrics, identify automation opportunities, coach teams, and liaise with stakeholders to deliver transformational value.

The Post Doctoral Scientist will analyze large-scale datasets related to cardiometabolic diseases, using statistical and computational techniques to identify genetic factors and support therapeutic development.

Lead product definition, research product-market fit, create process flows, UI mockups and documentation, coordinate with engineering on APIs and ML integrations, manage SDLC, prepare release notes and marketing materials, conduct demos and training, and support pricing/RFPs.

Create and review realistic, locale-specific robotics and natural sciences tasks in Marathi; apply scoring rubrics; review and score AI and human responses; produce gold-standard solutions and expert feedback.