Develop and maintain enterprise-grade ontologies (OWL/RDF) that model the complex domain of aerospace engineering, manufacturing, and supply chain.
Define the semantic schema for AI Agents, ensuring they share a common vocabulary when communicating across different business units.
Lead the architecture and deployment of our Enterprise Knowledge Graph.
Build data pipelines (Python) to ingest data from legacy ERP systems, PLM software, and unstructured documents into the graph.
Implement Entity Resolution and linking strategies to unify disparate data points across the organization.
Collaborate with AI Engineers to design "Context Windows" for LLMs.
Work on Causal Inference models: Structure data to help agents distinguish between correlation and causation.
Establish semantic standards and data governance policies for the AI ecosystem.

Requirements

Bachelor’s degree in Computer Science, Information Science, Mathematics, or related field with 10+ years of relevant experience; OR a Master's degree with 8+ years of relevant experience; OR a PhD in Computer Science, Information Science, Mathematics, or related field with 5+ years of relevant experience.
5+ years in Ontology Engineering, Knowledge Representation, or Data Modeling.
Proficiency in Graph Query Languages (SPARQL, Cypher, Gremlin).
Strong coding skills in Python (essential for our AI stack).
Experience with semantic web standards (RDF, OWL, SKOS, SHACL).
Experience working with LLMs, Vector Databases, and RAG architectures.
Experience in manufacturing, aerospace, or complex supply chain environments is a strong plus.
U.S. citizenship is required, as only U.S. citizens are authorized to access information under this program/contract.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

Ontology EngineeringKnowledge RepresentationData ModelingGraph Query LanguagesSPARQLCypherGremlinPythonSemantic Web StandardsRDF