Lead Knowledge Graph Engineer

We are seeking an experienced Lead Knowledge Graph Engineer to evaluate, optimize, and scale enterprise semantic data infrastructure. The ideal candidate will have deep expertise in Knowledge Graphs, Semantic Web technologies, ontology design, and GraphRAG architectures. This role will play a key part in bridging complex biomedical data with next-generation AI and Generative AI solutions., * Conduct technical health assessments of existing graph databases and cluster infrastructure.

• Design, review, and optimize RDF/OWL ontologies and Labeled Property Graph (LPG) schemas for enterprise scalability.
• Align enterprise data models with biomedical standards including MeSH, SNOMED, and UMLS.
• Improve graph database performance by optimizing data ingestion pipelines and query execution.
• Develop strategic architecture recommendations, including cloud migration and build-versus-buy assessments.
• Design and implement integrations between Knowledge Graphs and Large Language Models (LLMs) using GraphRAG frameworks.
• Partner with Research, Clinical, and business stakeholders to deliver scalable semantic AI solutions.

• 10+ years of experience working with Knowledge Graphs, Triple Stores, or Labeled Property Graphs.
• Strong expertise in RDF, SPARQL, OWL, and Semantic Web technologies.
• Hands-on experience with at least one RDF graph database such as Stardog, AnzoGraph, Blazegraph, or Apache Jena.
• Experience with property graph databases such as Neo4j or TigerGraph is preferred.
• Strong programming experience with Python and Java.
• Proficiency with graph query languages including SPARQL, Cypher, and Gremlin.
• Extensive experience in ontology design, semantic modeling, and Linked Data principles.
• Strong understanding of W3C Semantic Web standards and URI design strategies., * Experience with GraphRAG, Context Graphs, AI Agents, and Generative AI applications.
• Experience working with biomedical datasets such as ChEMBL, Ensembl, OBO Foundry, and Monarch Initiative.
• Pharmaceutical industry experience with biomedical data, gene-disease associations, chemistry structures, and CMC data.
• Experience with Azure or AWS cloud platforms.
• Knowledge of modern data platforms including Snowflake, Databricks, and Amazon Redshift.
• Familiarity with Agile tools such as Jira, Confluence, and ServiceNow.
• AWS Cloud Practitioner certification is a plus.

Mandatory Skills

• RDF
• SPARQL
• OWL
• Semantic Modeling
• Ontology Design
• Python
• Stardog / AnzoGraph / Blazegraph / Apache Jena
• Knowledge Graph Engineering

Nice to Have

• Neo4j or TigerGraph
• Biomedical Data Modeling
• GraphRAG
• Generative AI
• Azure
• Snowflake
• Databricks
• Tableau
• AWS Cloud Practitioner Certification