We are seeking a hands-on Ontology / Knowledge Graph Engineer to design, build, and maintain enterprise knowledge models supporting network automation, semantic search, and AI/agentic use cases. The ideal candidate combines strong RDF/OWL and graph engineering skills with practical experience converting complex, real-world data into well-defined, validated semantic models.

Key Responsibilities:

Design and evolve enterprise ontologies using RDF, RDFS, OWL, Turtle (TTL), and SHACL. Develop class/property hierarchies, relationships, taxonomies, namespaces, constraints, and reusable ontology patterns. Apply both schema-first and schema-late modeling approaches, including reverse-engineering models from existing data. Profile and model large, complex datasets from Excel/CSV, CIQ, configuration, inventory/CMDB, JSON, and relational sources. Define atomic vs. derived attributes, cross-field relationships, deterministic derivation rules, and validation rules. Develop

SPARQL 1.1

queries, competency questions, graph traversals, dependency/impact queries, and semantic validation. Build and maintain ontology ingestion, transformation, validation, and testing pipelines using Python. Work with RDF graph databases such as GraphDB/Stardog and understand Labeled Property Graph (LPG) technologies such as Neo4j. Support Ontology-RAG / GraphRAG / hybrid graph + vector retrieval and integration of knowledge graphs with AI agents. Partner with network/domain SMEs to translate implicit business and engineering knowledge into explicit, testable models. Maintain ontology artifacts, specifications, mappings, and validation rules in Git/CI-CD.

Required Skills & Experience:

Strong hands-on experience with RDF, RDFS, OWL, TTL, SHACL, SPARQL, and Semantic Web technologies. Practical knowledge of knowledge graphs, graph traversal, RDF stores, LPGs, and graph validation. Experience with schema-first and schema-late ontology development. Strong data modeling and reverse-engineering skills, including working with messy or incomplete source data. Experience with JSON Schema, relational data modeling, and mapping between relational/JSON and graph models.