Principal AI Architect for Enterprise Knowledge Graph

We’re building an AI system on top of a large, messy body of internal data documents, databases, and third-party feeds that currently don’t talk to each other. Our first RAG prototype works for simple lookups but falls apart on multi-hop questions and hallucinates when the answer spans multiple sources. We’re looking for an experienced Knowledge Graph & AI Engineer to take this from prototype to production. Scope: • Design and build a Neo4j knowledge graph ontology design, entity resolution, graph data modeling, Cypher queries • Upgrade our retrieval layer to GraphRAG (hybrid vector + graph retrieval, LangChain/LlamaIndex) • Build LLM agents grounded in the graph (OpenAI/Claude), with evaluation and guardrails • Deploy on Kubernetes with CI/CD, monitoring, and zero-downtime releases • Advise our team on architecture and roadmap as we scale Requirements: • Proven production experience with Neo4j, Cypher, and knowledge graphs • Strong Python, LLM/RAG, and machine learning background • Kubernetes, Docker, CI/CD experience must be able to ship, not just prototype • Experience in regulated or security-sensitive environments is a strong plus • Clear communicator who can work with both engineers and leadership To apply: Share a brief overview of a knowledge graph or GraphRAG system you’ve shipped, what the data looked like, and how you handled scale.