Knowledge Graph Engineer / Ontologist
Hartford, CTAll locationsHartford, CTCharlotte, NCNew York, NYNaperville, IL- W. Diehl RoadColumbus OH-Worth Ave Remote
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About the role
The Ontologist (Knowledge Graph Engineer) is responsible for designing, implementing, and operationalizing enterprise semantic models, ontologies, and knowledge representations that provide meaning, context, and explainability for AI‑driven analytics, agentic systems, and decision automation. This role ensures that business concepts, entities, relationships, and behaviors are explicitly modeled and consistently applied across data products and AI systems, enabling reuse, trust, reasoning, and scalable AI adoption across Customer, Operations, and Enterprise domains. This role is part of the Customer Data Ecosystem (CDE) and operates at the intersection of business semantics, data architecture, and AI enablement, translating complex domain knowledge into production‑ready semantic assets that are consumable by both humans and machines. This role can have a Hybrid or Remote work schedule. Candidates who live near one of our office locations will have the expectation of working in an office 3 days a week (Tuesday through Thursday) Candidates who do not live near an office will have a remote work arrangement, with the expectation of coming into an office as business needs arise. Candidates must be eligible to work in the US without company sponsorship.
What you'll bring
- 8–12+ years of hands-on experience in semantic layer architecture, ontology modeling, and knowledge graph design at enterprise scale.
- Deep, hands‑on expertise with RDF, OWL (OWL2), RDFS, SKOS, SPARQL (querying, optimization, semantic analytics), and W3C semantic web standards
- Proven experience designing and operating knowledge graphs at enterprise scale
- Hands‑on experience with graph or triple‑store technologies (e.g., Neo4j, Neptune, TigerGraph, Spanner Graph)
- Experience integrating knowledge graphs with LLMs, RAG pipelines, vector stores, and Agentic frameworks.
- Strong understanding of AI consumption patterns, including embeddings, grounding, and explainability
- Experience integrating semantic layers with data platforms, APIs, metadata systems, and AI pipelines
- Ability to translate complex domain knowledge into formal, machine‑readable semantic structures
- Strong understanding of context-aware data engineering and semantic interoperability.
- Proven ability to move from strategy → pilot → scaled enterprise capability.
- Strong executive influence and thought leadership in Agentic analytics and AI‑native data engineering.
- Hands-on experience with AWS, GCP, and Snowflake