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Specialized senior AI role at a recognizable firm increases candidate competition in Bangalore.
Advanced LLM, MLOps, and scientific ML skills are transferable but still industry-specific.
Explicit 10+ years and mandatory LLM, MLOps, and scientific ML skills create strict screening.
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Develop and deploy Retrieval-Augmented Generation (RAG) pipelines for enterprise AI using LangChain, LlamaIndex, and vector databases.
Design and operationalize agentic AI workflows leveraging Model Context Protocol (MCP) and integrate large language models via Azure OpenAI, Bedrock, and Anthropic APIs.
Build and manage MLOps pipelines using MLflow, SageMaker, Azure ML; transform unstructured R&D data into AI assets and apply Scientific Machine Learning to engineering problems.
10+ years of professional experience in relevant AI engineering or embedded systems fields.
Mandatory skills: LLM integration and fine-tuning, RAG pipelines (LangChain, LlamaIndex), MCP for agentic AI, vector databases (Pinecone, Weaviate, Chroma), MLOps tools (MLflow, SageMaker, Azure ML, Vertex AI), Python testing for ML pipelines.
Education: Bachelor or Master of Engineering degree.
Work Visa Sponsorship: Not available.
Experienced AI engineer with deep expertise in integrating and operationalizing LLMs and agentic systems in enterprise environments.
Technical operator skilled in advanced MLOps and managing end-to-end AI pipelines, including embedding unstructured R&D data.
Strong background in Scientific ML (PyTorch, JAX), indicating ability to apply cutting-edge ML research to practical engineering problems.