Top Machine Learning Engineer Jobs in Mumbai

Design, build, and deploy machine learning models for a payments platform, overseeing end-to-end solutions and experimentation, while monitoring models in production.

The Senior Software Engineer will lead the design and development of AI-driven solutions, mentor junior engineers, and optimize AI/ML models for various applications.

Design and architect production-grade ML and GenAI solutions on Databricks, including RAG and agentic systems, MLOps pipelines, training/inference optimization, model monitoring, and customer-facing technical enablement and mentorship.

Design and implement scalable end-to-end AI/ML solutions and data pipelines; define secure cloud and on-prem architectures; lead MLOps, CI/CD, model lifecycle management, data governance, and cross-functional collaboration to drive AI initiatives in manufacturing.

Lead design, development, and deployment of production-ready ML/AI systems using Python, PyTorch, LLMs and related tools. Drive model research, tuning, monitoring, cross-functional integration, and mentor junior engineers while prioritizing practical business impact.

Build and deploy production-grade generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with vector DBs, fine-tune and host LLMs, develop scalable FastAPI backends, optimize latency/cost, and containerize services for production.

Design, build, and maintain AI platform and ML pipelines by assembling LLMs and agentic workflows, integrating assistants with systems, prototyping in low-code and code, writing tests and CI/CD, and iterating with stakeholders on domusAI, domiAI, domusDocs and multi-agent frameworks.

Design, build, and scale production Generative AI systems: multi-agent workflows, advanced RAG pipelines, model fine-tuning and quantization, LLM integration and swapping, high-performance FastAPI backends, vector search and retrieval, containerized deployment, and monitoring for latency, cost, and reliability.

Design and implement end-to-end machine learning solutions: analyze use cases, build statistical and ML models, prepare and enrich data, implement scalable PySpark models in Databricks, and collaborate with product and engineering to productionize analytics for personalization and business insights.

Lead design and production of face recognition and biometric CV systems. Build, train, optimize models (PyTorch/TensorFlow/JAX), own end-to-end ML pipelines (Airflow), ensure fairness, deploy low-latency inference on AWS, and mentor engineers.

Design, build, deploy, and monitor production-grade AI/ML solutions. Develop data pipelines, train and optimize models, implement MLOps and CI/CD, integrate AI into enterprise applications, mentor junior engineers, and ensure security, governance, and responsible-AI practices.

Lead design, development, and production deployment of enterprise-scale ML and GenAI solutions (LLMs, RAG, recommendation systems). Architect scalable inference and training pipelines, mentor MLEs, collaborate with stakeholders, ensure security/compliance, and measure and optimize model performance and business impact.

The role seeks an AI Lead to drive Generative AI and Computer Vision projects, focusing on precise visual transformations and model training while mentoring engineering teams in best practices.

The Senior Machine Learning Engineer will design, develop, and deploy AI systems, focusing on Generative AI and LLM integration across various business operations, ensuring high performance and scalability.

The Associate Architect will lead the development and deployment of machine learning solutions on Azure, manage the ML lifecycle, and optimize existing models while providing technical leadership to project teams.

Design, implement, and evaluate mathematical, econometric, and machine learning models for Nielsen's analytics platforms. Build scalable web-based analytical solutions in partnership with software engineers, create visualizations, document methodologies, and present results internally and externally.

Design and deliver end-to-end GenAI and ML solutions on AWS: build SageMaker pipelines, fine-tune LLMs (Llama2), implement RAG with vector stores (OpenSearch/Elasticsearch), develop LangChain-based LLM apps, perform prompt engineering, model evaluation and optimization, and collaborate with cross-functional teams to integrate models into production.

Maintain and optimize production ML algorithms and services: triage/fix bugs, profile and improve performance, implement parallelism, build and maintain REST APIs (Django/DRF or FastAPI), test/benchmark scalability and efficiency, ensure code quality (tests, type hints, CI/CD), and collaborate with product/data/QA to meet SLAs.

Design, train, and productionize state-of-the-art object detection systems. Build robust training pipelines, perform error analysis and ablations, optimize inference for latency and memory, and deliver production services with Docker, CI/CD, and APIs. Implement testing, monitoring, model versioning, and retraining pipelines. Rapidly prototype research ideas into deployable components and maintain clear technical documentation.

Design, build, and deploy production-grade Generative AI platform components across GCP/Azure. Implement RAG pipelines, multi-agent architectures, LLM gateway and execution runtimes, GenAIOps/MLOps practices, AgentOps, and platform CI/CD/infrastructure. Collaborate with implementation partners and LBU teams to deliver reusable, secure, and compliant GenAI services.

The Senior Machine Learning Engineer will design, train, and deploy ML models for optimizing revenue in advertising, taking ownership of product development and leading the ML discipline.

The Senior Engineer will develop and deploy machine learning models and workflows, mentor junior engineers, and collaborate with cross-functional teams to improve patient care through innovative solutions.

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, 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.

Train and deploy small, task-specific ML models for realtime English conversational-skill applications. Create datasets and task-specific evaluations, improve internal SOTA, develop ASR and speech-feedback metrics, iterate experiments, ship production systems, and collaborate cross-functionally while helping hire as the team grows.