Associate Director, Semantic & Knowledge Engineering (2 Openings)
Novartis
August 24, 2026
Remote friendly (East Hanover, NJ)
United States
IT
Key Responsibilities
- Design, develop, and maintain enterprise ontologies, taxonomies, business vocabularies, and semantic models to align meaning across products, data, and AI.
- Build and enhance knowledge graphs, metadata services, and semantic APIs supporting AI, analytics, enterprise applications, and decision enablement.
- Partner with Product, Applied AI, Analytics Engineering, and Platform Engineering to embed semantic capabilities into products/workflows and reusable patterns.
- Enable semantic foundations for RAG, agentic AI, contextual search, reasoning engines, enterprise search, analytics consistency, and interoperability.
- Implement metadata governance, semantic quality controls, stewardship practices, and lifecycle management for enterprise knowledge assets.
- Evaluate emerging semantic technologies/standards and evolve enterprise knowledge architecture; develop reusable engineering patterns and technical guidance.
- Mentor semantic/knowledge engineers and communicate trade-offs, risks, dependencies, and recommendations to stakeholders.
Essential Requirements
- Bachelor’s degree in CS/Info Science/AI/Data Science/Bioinformatics or related (advanced degree preferred).
- 6+ years in semantic technologies, knowledge engineering, metadata/data/information architecture, data products, or AI-enabling data platforms.
- Hands-on: ontologies/taxonomies/controlled vocabularies, semantic models; RDF/OWL, SKOS, SPARQL; graph databases; knowledge graphs; metadata catalogs; data lineage; semantic APIs/services.
- Experience enabling AI grounding, RAG/GraphRAG, enterprise/contextual search, reasoning engines, interoperability, and reusable product capabilities.
- Knowledge of data governance, stewardship/provenance, quality controls, access/security, lifecycle management, privacy and compliance.
Desirable Requirements
- Experience in regulated environments (pharma/healthcare/life sciences).
- Generative/agentic AI and LLM/RAG/vector/graph-enhanced retrieval; FAIR data; master/reference data modernization.
- Cloud ecosystem, semantic layer/API services, integrations; mentoring and standards/community contributions.
- Relevant certification or advanced degree.
Compensation/Benefits (as stated)
- Salary expected: $176,400–$327,600/year; performance cash incentive; eligibility for annual equity awards (level-dependent). Comprehensive benefits and time off for US-eligible employees.
- Design, develop, and maintain enterprise ontologies, taxonomies, business vocabularies, and semantic models to align meaning across products, data, and AI.
- Build and enhance knowledge graphs, metadata services, and semantic APIs supporting AI, analytics, enterprise applications, and decision enablement.
- Partner with Product, Applied AI, Analytics Engineering, and Platform Engineering to embed semantic capabilities into products/workflows and reusable patterns.
- Enable semantic foundations for RAG, agentic AI, contextual search, reasoning engines, enterprise search, analytics consistency, and interoperability.
- Implement metadata governance, semantic quality controls, stewardship practices, and lifecycle management for enterprise knowledge assets.
- Evaluate emerging semantic technologies/standards and evolve enterprise knowledge architecture; develop reusable engineering patterns and technical guidance.
- Mentor semantic/knowledge engineers and communicate trade-offs, risks, dependencies, and recommendations to stakeholders.
Essential Requirements
- Bachelor’s degree in CS/Info Science/AI/Data Science/Bioinformatics or related (advanced degree preferred).
- 6+ years in semantic technologies, knowledge engineering, metadata/data/information architecture, data products, or AI-enabling data platforms.
- Hands-on: ontologies/taxonomies/controlled vocabularies, semantic models; RDF/OWL, SKOS, SPARQL; graph databases; knowledge graphs; metadata catalogs; data lineage; semantic APIs/services.
- Experience enabling AI grounding, RAG/GraphRAG, enterprise/contextual search, reasoning engines, interoperability, and reusable product capabilities.
- Knowledge of data governance, stewardship/provenance, quality controls, access/security, lifecycle management, privacy and compliance.
Desirable Requirements
- Experience in regulated environments (pharma/healthcare/life sciences).
- Generative/agentic AI and LLM/RAG/vector/graph-enhanced retrieval; FAIR data; master/reference data modernization.
- Cloud ecosystem, semantic layer/API services, integrations; mentoring and standards/community contributions.
- Relevant certification or advanced degree.
Compensation/Benefits (as stated)
- Salary expected: $176,400–$327,600/year; performance cash incentive; eligibility for annual equity awards (level-dependent). Comprehensive benefits and time off for US-eligible employees.