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Tier-1 brand, mid-level (5+) requirement, and metro location increase competition despite niche LLM/MLOps skills.
Specialized LLM/MLOps skills transfer across industries, though enterprise advisory experience is preferred.
Mandatory 5+ years plus specific LLM, RAG and MLOps technologies impose strict shortlisting filters.
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Develop and deploy Retrieval-Augmented Generation (RAG) pipelines using LangChain, LlamaIndex, and vector databases for enterprise AI applications.
Design and implement Large Language Model (LLM) integrations and agentic workflows using Model Context Protocol (MCP) with platforms such as Azure OpenAI, Bedrock, and Anthropic APIs.
Build and operationalize MLOps pipelines using MLflow, SageMaker, Azure ML or Vertex AI to transform unstructured R&D data into queryable AI assets and collaborate with domain experts to apply Scientific ML methods.
Minimum 5+ years of professional experience in AI engineering or related roles.
Strong hands-on experience with LLM integration and fine-tuning, RAG pipelines (LangChain, LlamaIndex), vector databases (Pinecone, Weaviate, Chroma), and MLOps tools (MLflow, SageMaker, Azure ML, Vertex AI).
Proficiency in Python for ML pipeline testing and model validation.
Education: Bachelor’s or Master’s Degree (field not explicitly specified).
Experienced in designing and operationalizing advanced AI pipelines specifically for enterprise-level AI and digital engineering.
Skilled in integrating and managing workflows involving multiple AI platforms and APIs, showing a strong domain expertise in AI tooling ecosystems.
Able to collaborate effectively with domain experts to leverage Scientific ML techniques such as PyTorch, JAX, and graph neural networks.