Capgemini - Principal Data Architect - AI Knowledge Platform

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 environments.

About 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 Platforms.

Responsibilities :

- 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 development.

Preferred 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.