Top Machine Learning Engineer Jobs in Mumbai

Design, build, and scale ML and generative AI solutions on the Elastic Stack: anomaly detection, data frame analytics, forecasting, vector/semantic/hybrid search, ELSER, and RAG. Integrate embeddings and LLMs, deploy transformer models via Eland, create inference ingest pipelines, design data models and ingestion flows, tune retrieval and relevance, and build Kibana dashboards and ML jobs for monitoring and alerting while partnering to operationalize features at scale.

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

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.

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.

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.

Develop and refine voice-optimized prompts and agentic LLM workflows for finance use cases. Build RAG grounding, integrate LLMs with voice/telephony (LiveKit), implement feedback loops, A/B testing, fine-tune models, and collaborate on production ML services and OCR/chatbot components.

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.

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.

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.

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 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.

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.

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, develop, and deploy ML models on Google Cloud (Vertex AI). Process and feature-engineer large datasets in BigQuery, integrate ML pipelines with GCP services, collaborate with stakeholders, and produce analytical reports and visualizations to support data-driven decisions.