Ontology Systems Engineer

General Dynamics Mission Systems, Inc•,

2d•$157,487 - $174,713•Remote

About The Position

This role is crucial for building the connective tissue between multiple AI modernization efforts, enabling cross-domain AI reasoning. The Ontology Systems Engineer will design and maintain the enterprise knowledge architecture, ensuring consistency and findability of data across various domains. This position requires a blend of systems thinking and business fluency to understand and connect diverse business processes and systems.

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.
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

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.
Ensure that business concepts defined in one pod are compatible with concepts in other pods.
Resolve naming conflicts, semantic overlaps, and definitional inconsistencies.
Establish enterprise-wide standards for metadata, tagging, classification, and search structures.
Build the knowledge infrastructure that makes enterprise data findable, reusable, and machine-readable.
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 of the business glossary.
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.