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# Ontology & Knowledge Graph AI Architect

- **Company:** Pearson
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Software Applications, Graph Database, Machine Learning, Natural Language Processing, Neo4j, Web Ontology Language, Resource Description Framework (RDF), Search Technologies, SPARQL, Large Language Models, Generative AI
- **Published:** August 25, 2026
- **Apply:** https://www.dice.com/job-detail/a4bca6bc-5fc8-43f3-a97b-7c06c0fa83c4

## About the Role

* 10+ years of experience in Ontology Engineering, Semantic Technologies, Knowledge Graphs, AI/ML, or Data Science.
 * Deep expertise in ontology design, semantic data modeling, taxonomies, RDF, OWL, SKOS, SPARQL, and graph-based architectures.
 * Hands-on experience building and managing enterprise-scale knowledge graphs and metadata ecosystems.
 * Strong background in Machine Learning, NLP, Generative AI, and automated knowledge extraction/classification.
 * Experience with entity resolution, graph embeddings, semantic search, reasoning, and ontology mapping.
 * Proficiency with graph databases and semantic platforms such as Neo4j, Stardog, GraphDB, Amazon Neptune, or similar.
 * Ability to engage with senior leadership and provide architectural and strategic consulting.

Preferred Experience

 * Exposure to learning, education, workforce skills, competency frameworks, or talent intelligence platforms.
 * Experience leveraging knowledge graphs to enable LLM and AI-powered applications.

## Description

* Design and define an enterprise-wide ontology framework spanning learner, assessment, skills, workforce, and educational content domains.
 * Architect and implement large-scale federated knowledge graph solutions integrating multiple existing knowledge graphs and data sources.
 * Develop AI/ML-driven approaches for automated ontology creation, entity classification, ontology alignment, relationship discovery, and semantic enrichment.
 * Build semantic models, reasoning frameworks, and inference mechanisms to uncover relationships across domains.
 * Collaborate with product, data science, engineering, and business stakeholders to establish a reusable AI-ready knowledge layer.
 * Lead the initial PoC focused on Learner Skills to Job Skills Mapping, leveraging assessment outcomes, competencies, skills ontologies, and career pathways.
 * Provide strategic guidance on enterprise knowledge graph architecture, governance, scalability, and future AI adoption.


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