Knowledge Graph & Ontology Engineer

Job Description

A client of Insight Global is seeking an experienced Knowledge Graph & Ontology Engineer to design, implement, and govern the knowledge representation layer for next-generation AI systems. This role builds the foundational knowledge structures—ontologies, semantic models, knowledge graphs, provenance, and data fusion patterns—that enable AI agents and LLM applications to reason over enterprise knowledge reliably.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances.

If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to  To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy:

• Proven experience building knowledge graphs, semantic data models, and/or enterprise knowledge bases.
• Experience with semantic technologies and standards (as applicable): RDF, OWL, SPARQL (or equivalent graph/ontology concepts).
• Strong foundations in data modeling, entity resolution/canonicalization, and schema governance.
• Proficiency in Python and working with data pipelines (in collaboration with data engineering). Experience designing agent memory representations (episodic/semantic memory patterns, long-term context).
• Familiarity with LLM grounding patterns (provenance, citations, trust signals).
• Experience with graph databases and tooling (e.g., Neo4j/AWS Neptune equivalents).
• Experience with data-centric AI and training data quality assessment.