3 of 3 Retrieval-Augmented Generation Jobs in York

regulatory data in vector space Build and operate vector database infrastructure for semantic search and similarity retrieval Implement RAG (Retrieval-Augmented Generation) architectures that ground LLM outputs in Comply’s proprietary data Evaluate and integrate LLM tooling and frameworks appropriate to Comply … databases (e.g. Jena Fuseki, Neo4j, Amazon Neptune, or equivalent) Experience with vector databases and embedding pipelines (e.g. Pinecone, Weaviate, Qdrant, pgvector) Practical experience implementing RAG architectures or LLM-integrated data pipelines Familiarity with semantic web standards — JSON-LD, RDF, OWL, or SKOS Strong Python skills and experience with data pipeline ...

roadmaps and target state architectures within large, complex organisations. Deep expertise across AI and GenAI technologies, including agentic AI, multi-agent systems, LLM platforms, RAG architectures, semantic search and enterprise AI services. Strong experience designing secure, scalable AI solutions on AWS and Azure, alongside modern cloud-native ...

Decision Intelligence driving predictive analytics, propensity, forecasting, experimentation, causal thinking, optimization. GenAI/Agent Engineering that is essential to the enterprise agent strategy – MCP, RAG, embeddings/vector search, tool calling, agent architecture, evaluation, grounding, orchestration. Architect and govern the Enterprise Agentic AI Strategy, deploying multi-agent frameworks … end. Design internal AI tools and agents (e.g., AI Finance Bot, domain-specific LLM applications) and lead the roadmap for next-generation agentic platforms as emerging capabilities mature. Drive Data Governance & AI Readiness – quality, lineage, observability, catalog, access controls, PII, retention, AI data governance. Provide business context ...