Top Machine Learning Engineer Jobs in Mumbai

Design, implement, and lead large-scale generative AI and ML systems (LLM fine-tuning, RAG, agents, Graph ML). Build REST APIs, deploy with Docker/Kubernetes, optimize big-data workflows, mentor engineers, and operate ML services on cloud platforms.

Design, develop, and deploy ML models and pipelines; preprocess data and engineer features; implement MLOps (Docker, Kubernetes, CI/CD); collaborate with stakeholders to translate business problems into AI solutions; monitor and optimize model performance and maintain documentation and governance.

Lead design, operation, and reliability of large-scale AI/HPC clusters. Manage day-to-day operations, incident response, automation, performance tuning, scheduler and storage optimization, and collaborate with researchers and global teams to improve GPU-accelerated infrastructure.

Design, train, optimize, and productionize deep learning models for speech/audio and computer vision. Build scalable training pipelines, perform model inference optimization, integrate models into production services, monitor performance, and collaborate with software and product teams to deliver low-latency, reliable ML features.

Lead architecture and engineering of enterprise-scale Generative AI and data platforms. Design reusable platform services for end-to-end AI/ML lifecycle, build agentic applications and event-driven systems, enforce CI/CD and DevSecOps, ensure security/governance/observability, mentor engineers, and drive platform modernization using cloud-native, containerized, and distributed technologies.

Lead platform automation for enterprise Generative AI and data platforms: design and build IaC, CI/CD, observability, deployment automation, and self-service capabilities to accelerate AI/ML lifecycle, reliability, governance, and developer productivity across cloud-native distributed environments.

Lead end-to-end design, build, deployment and iteration of AI-agent solutions. Embed with clients to map workflows, architect multi-agent systems, integrate enterprise systems and RAG pipelines, implement guardrails and observability, and liaise with non-technical stakeholders to drive adoption.

Design, automate, and manage end-to-end ML pipelines and scalable infrastructure on Azure. Build CI/CD with GitHub Actions, containerize models with Docker and AKS, monitor model lifecycle and data drift, implement cost controls, and collaborate with data science, DevOps, and IT to deploy resilient, production-grade ML solutions.

Lead architecture and development of agentic AI solutions for optical networks: design agent orchestration, memory, tool integration, guardrails, and human-in-the-loop workflows; convert processes into autonomous/semi-autonomous automation; document designs and best practices for scalable delivery.

The role involves designing and deploying machine learning models, ensuring model performance, and optimizing ML infrastructure on GCP, collaborating closely with data engineers and scientists.