Capgemini - Principal Data Architect - AI Knowledge Platform in Pune, India

About the Company :We are seeking a Principal Knowledge & Data Platform Lead to architect the knowledge, retrieval, and context foundation that enables AI agents to reason, retrieve, learn, and operate safely in highly regulated https://jobeax.com/link/f1IRFfA7PZ1XSGcp the Role :This is a high-impact individual contributor role for a senior technologist who combines deep expertise in Knowledge Graphs, RAG, Context Engineering, AI Knowledge Management, and Enterprise Data https://jobeax.com/link/q0mk26XkVeAsJ7MH :- Enterprise Knowledge Graph architecture, ontology design, semantic modeling, and graph-based retrieval.- End-to-end RAG optimization including embeddings, hybrid search, GraphRAG, reranking, retrieval evaluation, and grounding quality.- Context Engineering frameworks that determine what information enters an agent's context window, when, and at what cost.- AI Knowledge Management lifecycle covering curation, provenance, versioning, freshness, governance, and auditability.- Agent Memory platforms supporting episodic and long-term memory across AI systems.- Permission-aware retrieval with tenant isolation, document-level security, compliance controls, and audit trails.- Data flywheel architecture transforming production data into training and evaluation assets for future LLM and SLM https://jobeax.com/link/uW4ozJESYI73KG83 Skills :- Deep expertise in Neo4j, Knowledge Graphs, Ontologies, and Semantic Data Modeling.- Proven experience building and scaling production-grade RAG systems.- Strong understanding of GraphRAG, Hybrid Retrieval, Embedding Strategies, and Retrieval Evaluation.- Experience with Context Engineering, Agent Memory, and Agentic AI architectures.- Knowledge management expertise focused on AI consumption rather than traditional enterprise search.- Experience working within regulated industries such as Financial Services, Banking, Insurance, Healthcare, or GDPR-governed environments.- Technical leadership ability to define standards, influence architecture decisions, and drive adoption across multiple engineering teams.- GraphRAG, LangGraph, LangChain, MCP (Model Context Protocol).- Vector Databases, Enterprise Search, AI Observability.- LLM Evaluation Frameworks.- AI Platform Engineering & Agentic AI Systems.- Financial Services, Payments, Insurance, or Healthcare Domain Experience. (ref:hirist.tech)