Senior Ontology / Semantic AI Engineer
Location: New York City (Hybrid – 2–3 days onsite per week)
Duration: 6+ Month Contract (Potential Extension)
Industry: Life Sciences / Pharma
Overview
We are partnering with a leading global enterprise in the life sciences sector to support a strategic AI enablement initiative. This role sits at the intersection of semantic modeling, knowledge graphs, and applied AI.
We are seeking a senior-level Ontology / Semantic Engineer who can design and implement enterprise semantic layers that directly support AI-driven use cases, including RAG, LLM-powered systems, and AI-ready data products.
This is a highly collaborative role requiring both deep technical expertise and strong stakeholder engagement skills.
Key Responsibilities
• Design and implement ontologies and semantic data models to support AI-driven applications
• Integrate knowledge graphs with AI/LLM-based systems (RAG, semantic search, contextual retrieval, etc.)
• Develop conceptual, logical, and physical semantic models aligned to enterprise data standards
• Collaborate with business stakeholders to lead workshops and define domain concepts and taxonomies
• Translate business requirements into reusable semantic data products
• Partner with data engineering and AI teams to operationalize semantic layers in production environments
• Ensure governance, metadata alignment, and version control across ontology frameworks
Required Qualifications
• 8+ years of experience in ontology engineering, semantic modeling, or knowledge graph development
• Hands-on expertise with OWL, RDF, SHACL, SPARQL, and semantic standards
• Experience integrating ontologies with AI systems (RAG, LLMs, GraphRAG, semantic AI)
• Proven ability to work directly with business stakeholders and lead modeling workshops
• Experience designing semantic layers that support AI-ready data products
• Prior experience in Pharma / Life Sciences (strongly preferred)
• Ability to work onsite in NYC 2–3 days per week
Nice to Have
• Experience with Neo4j, Stardog, GraphDB, or similar graph technologies
• Familiarity with FHIR, SNOMED, RxNorm, or other life sciences standards
• Experience with LangChain, LlamaIndex, or other LLM orchestration frameworks
Why This Role?
This is a rare opportunity to build semantic foundations that directly power enterprise AI systems within a regulated life sciences environment. The work is highly visible, technically challenging, and directly tied to AI transformation initiatives.
If you have deep semantic modeling expertise and experience integrating ontology with AI systems, we’d love to speak with you.