Lead Knowledge Graph Engineer

• 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.

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.