Knowledge Graph & GenAI Data Scientist

Knowledge Graph & GenAI Lead London | Remote Up to £700 per day inside IR35 Global AI & analytics firm operating at the intersection of knowledge graphs, generative AI, and enterprise transformation. This role requires you to embed graph intelligence into mission-critical systems enabling explainable AI, unified data views, and advanced reasoning across regulated industries and industrial domains. You will drive the design, build, and deployment of knowledge graph + GenAI systems for high-impact clients. You’ll be part of a small elite team, bridging data, AI, and business outcomes — from model scoping through to production launch. • Ship full systems • Operate in regulated or industrial domains (e.g. manufacturing, life sciences, public sector) • Embed client strategy and technical teams • Stretching the frontier: hybrid KG + AI, graph + reasoning + M 🛠 Responsibilities • Lead KG schema & ontology design across domains (assets, risk, supply chain, compliance) • Build ingestion pipelines (ETL / streaming / CDC) and entity resolution for graph population • Author complex queries (Cypher, GSQL, AQL, SPARQL etc. depending on stack) • Integrate knowledge graph retrieval & reasoning into LLM / RAG / GraphRAG systems • Develop and evaluate graph ML / embedding models (link prediction, anomaly detection) • Optimize graph performance, scaling, and query efficiency • Liaise with client stakeholders: translate business problems into graph solutions • Mentor junior engineers, contribute to propositions, and support POCs 📋 Must-Have Skills & Experience • 5+ years in engineering, data, or AI roles • Deep experience with at least one graph technology: Neo4j, TigerGraph, ArangoDB, OrientDB, or Stardog • Proficiency in query languages (Cypher, GSQL, AQL, SPARQL, etc.) • Strong background in pipelines, ETL, and entity resolution • Exposure to integrating KG + LLM or RAG architectures • Experience with graph algorithms, embeddings, or GNNs • Cloud & production engineering literacy (AWS/Azure/GCP, containerization, CI/CD) • Excellent communication skills — able to explain complex graph/AI concepts to non-technical audiences ✅ Nice-to-Have / Bonus Assets • Experience with GraphRAG or KG-backed LLM retrieval • Semantic web / ontology skills (RDF/OWL/SHACL) • Prior consulting or client delivery background • Graph visualization / UI experience (Linkurious, Bloom, Ogma) • Graph DB certifications (Neo4j, Stardog, etc.) • High visibility & critical client impact • Exposure to cutting-edge hybrid AI / KG architectures • Autonomy, ownership, and fast learning • Competitive compensation + meaningful equity or bonus scheme • Flexible / hybrid work arrangement