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# Lead Knowledge Graph Engineer

- **Company:** Upbeat Ideas UK Ltd
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Electronic Data Capture, Graph Database, Python (Programming Language), Linked Data, Neo4j, Performance Tuning, Semantic Web, SPARQL, Large Language Models
- **Published:** July 8, 2026
- **Apply:** https://www.totaljobs.com/job/knowledge-engineer/upbeat-ideas-uk-ltd-job107650695

## About the Role

Graph Expertise: 10+ years of engineering experience with Graph Databases, Triple Stores, or Labeled Property Graphs.
Technical Stack Preferences

 * Graph Databases: Stardog, AnzoGraph, or Neo4j
 * Query & Programming Languages: SPARQL, Cypher, Gremlin, Python, and Java
 * AI Tools: Any Vibe Coding Tool (Claude Code OR GHCP)
Pharma Domain Knowledge -

 * Pharma Domain Knowledge: Proven track record handling biomedical data like gene-disease associations and chemistry structures.
 * CMC Data Familiarity: Experience modeling Chemistry Manufacturing and Control data types, including product journeys and electronic data capture logs.
 * Bio-Ontologies & Datasets: OBO Foundry, ChEMBL, Ensembl, and Monarch Initiative
 * Advanced AI/GenAI: Hands-on experience designing and executing Graph RAGs, Context Graphs, Agents
 * Semantic Web Standards: Deep understanding of W3C standards, Linked Data principles, and URI minting strategies.

## Description

* Platform Assessment: Conduct comprehensive technical health checks on existing graph databases and cluster infrastructure.
 * Ontology & Schema Review: Evaluate RDF/OWL and Labeled Property Graph (LPG) schemas for enterprise scalability.
 * Standards Alignment: Map internal data frameworks to biomedical standards like MeSH, SNOMED, and UMLS.
 * Performance Optimization: Eliminate data bottlenecks across real-time ingestion pipelines and complex query execution.
 * Strategic Roadmap: Author comprehensive "Way Forward" reports detailing cloud migration and build-vs-buy decisions.
 * AI & LLM Integration: Design infrastructure to connect knowledge graphs with Large Language Models using GraphRAG frameworks.
 * Stakeholder Alignment: Translate technical graph concepts into clear business value for Research and Clinical teams.


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