Ontology Systems Engineer

General Dynamics Mission Systems, Inc•,

7d•$157,487 - $174,713•Remote

About The Position

This role is for an Ontology Systems Engineer who will be responsible for enterprise knowledge architecture, ensuring cross-domain consistency, establishing metadata and tagging standards, governing the enterprise business glossary, and designing knowledge repository architecture. The position is crucial for connecting multiple AI modernization efforts, enabling cross-domain AI reasoning, and requires a blend of systems thinking and business fluency. The engineer will work with AI modernization efforts, ensuring that agents can discover and traverse enterprise knowledge programmatically. This role is distinct from pod-specific data modeling, AI application development, and enterprise system administration or data engineering pipelines.

Requirements

Bachelor’s degree in Systems Engineering, Computer Science, Information Science, or a related field, plus 8 years of experience; or Master’s degree plus 6 years of experience
Experience designing enterprise-level data architectures, knowledge models, or information taxonomies that span multiple business domains
Strong understanding of ontology and semantic modeling concepts — you can work fluently with knowledge graph engineers and data modelers
Systems engineering mindset — you think about interfaces, dependencies, integration points, and emergent behavior across interconnected systems
Experience working across organizational boundaries — you have built consensus on shared standards across teams that had their own ways of doing things
Strong communication skills — you can explain data relationships to business stakeholders and architectural constraints to engineers
U.S. citizenship required.
Department of Defense Secret security clearance is required at time of hire.

Nice To Haves

Experience with knowledge graphs, semantic web technologies, or enterprise taxonomy management
Experience with enterprise data platforms (Palantir Foundry, Snowflake, or similar) and their ontology or semantic layer capabilities
Background in manufacturing, defense, or complex enterprise environments with multiple interacting business systems
Experience defining metadata standards, business glossaries, or data governance frameworks at an enterprise level
Familiarity with how AI/LLM systems consume structured knowledge — RAG architectures, knowledge-grounded reasoning, semantic search

Responsibilities

Enterprise knowledge architecture: Define and maintain the overarching structure that connects domain-specific ontologies across pods. Manage the relationships between business vocabularies, taxonomies, and data models at the enterprise level.
Cross-domain consistency: Ensure that business concepts defined in one pod are compatible with concepts in other pods. Resolve naming conflicts, semantic overlaps, and definitional inconsistencies before they become integration problems.
Metadata and tagging standards: Establish enterprise-wide standards for metadata, tagging, classification, and search structures. Build the knowledge infrastructure that makes enterprise data findable, reusable, and machine-readable.
Business glossary governance: Own the enterprise business glossary — the authoritative source for what terms mean across the organization. Work with data owners and business stewards to maintain accuracy.
Knowledge repository architecture: Design the structures that store and expose enterprise knowledge — knowledge graphs, semantic catalogs, taxonomy services. Ensure AI agents can discover and traverse enterprise knowledge programmatically.