Top Machine Learning Jobs in Pune

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, build, and deploy AI agents and machine learning models; integrate ML/NLP into full-stack applications; develop APIs and data pipelines; deploy and manage services on AWS; collaborate with frontend/backend teams and maintain CI/CD, databases, and production monitoring.

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 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.

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, develop, fine-tune, and productionize ML and Generative AI/LLM solutions. Build end-to-end ML pipelines (data ingestion, modeling, deployment, monitoring), implement RAG and vector DB architectures, apply prompt/context engineering and MLOps best practices, and collaborate with cross-functional teams to optimize models for performance, latency, scalability, and cost.

Design, build, and deliver enterprise-grade Generative AI and AI/ML platform capabilities: reusable services, APIs, microservices, RAG and agentic solutions, model lifecycle tooling, inference and monitoring, event-driven streaming with Kafka, CI/CD and DevOps automation, and platform observability to accelerate AI adoption across business domains.

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, 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.

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 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.

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.