Ontology Architect and Modeling Specialist | SME
Listed on 2026-11-04
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IT/Tech
Information & Knowledge Management, Data Engineering, AI Engineer (Applied/Software)
Location: Chantilly, VA
Security Clearance: Active TS or higher [Required]
Job Type: Full-Time
Target Salary Range*:$129,000 - $205,000
* This represents the potential salary range for this position depending on education level, years of experience and/or certifications in addition to other position specific requirements which may impact salary
Position OverviewThe Ontology Architect and Knowledge Modeling Specialist (SME) designs and implements enterprise knowledge graphs and ontological models that transform unstructured data into interconnected, machine-readable intelligence. This role develops semantic architectures supporting interoperability, inference, metadata management, and context-aware AI. Working with data scientists, software engineers, and AI architects, the specialist integrates semantic frameworks with enterprise graph databases and modern AI pipelines while establishing governance and validation practices to ensure data quality.
Key Responsibilities Semantic Architecture and Knowledge Modeling- Design and implement enterprise knowledge graphs and ontological models.
- Develop domain-specific ontologies, taxonomies, and controlled vocabularies using OWL, RDF, and SHACL.
- Model complex data relationships and business logic.
- Translate real-world business domains into structured semantic models supporting entity resolution, semantic search, and context-aware AI.
- Design semantic schemas enabling cross-system interoperability and advanced inference.
- Collaborate with data scientists, software engineers, and AI architects to integrate semantic frameworks with GraphDB and Neo4j.
- Integrate semantic models with AI pipelines, including retrieval-augmented generation (RAG) and vector search structures.
- Establish data governance practices and validation workflows.
- Minimum of 10 years of experience.
- Semantic architecture, enterprise knowledge graph design, and ontology development.
- Domain-specific taxonomies and controlled vocabularies.
- Semantic web standards, including OWL, RDF, and SHACL.
- Semantic schema design supporting interoperability, inference, and metadata management.
- Enterprise graph databases, including GraphDB and Neo4j.
- Integration of semantic frameworks with RAG pipelines and vector search structures.
- Data governance, validation workflows, and data harmonization.
- Semantic modeling supporting entity resolution, semantic search, and context-aware AI.
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