Senior Semantic Engineer

About the Role We are seeking a Senior Semantic Engineer to design and implement semantic data frameworks that provide a shared structure for enterprise data. You will focus on building ontologies and knowledge graphs, enforcing data quality, and collaborating with AI teams to integrate semantic structures into intelligent applications. The role is industry-agnostic and requires strong semantic web expertise to apply these techniques across enterprise contexts.

What You'll Do

• Design, develop, and maintain ontologies (OWL/RDF or similar) modeling key enterprise data domains and relationships; collaborate with domain experts to capture concepts and validate ontology accuracy.
• Build and manage enterprise knowledge graphs, linking data sources into a unified graph model; configure graph databases to optimize for query performance and scalability.
• Create and optimize SPARQL queries and endpoints for advanced search and analytics.
• Implement semantic validation rules and data quality checks (SHACL or OWL constraints) to ensure data integrity.
• Integrate ontology and knowledge graph into data pipelines, APIs, and databases; work with software engineers and IT teams.
• Collaborate with cross-functional teams including AI/ML and business analysts; communicate semantic concepts to non-technical stakeholders and provide documentation.
• Partner with AI agents/LLM teams to enable context-aware reasoning and improved knowledge retrieval in AI workflows.

What We're Looking For

• Ontology Design & Maintenance: Design, develop, and maintain ontologies using OWL/RDF; strong semantic web proficiency.
• Semantic Web Proficiency: Hands-on experience with OWL, RDF, SPARQL.
• Knowledge Graph Experience: Building or maintaining enterprise knowledge graphs or linked data systems.
• Data Modelling & Integration: Data modelling, architecture, integrating heterogeneous data sources; map relational/NoSQL data to ontology.
• Programming Skills: Proficiency in Python, Java, or similar.
• Nice to Have: Metadata Standards familiarity (Dublin Core, schema.org) and related vocabularies.
• AI and LLM Integration: Experience with AI agents or large language models leveraging ontologies/knowledge graphs.
• Enterprise System Integration: Experience integrating semantic technologies into existing enterprise systems.
• Tools & Platforms: Hands-on experience with ontology and knowledge graph tools.

Nice to Have

• Metadata standards familiarity: Dublin Core, schema.org, or other industry ontologies/taxonomies.
• Experience applying metadata standards to annotate or integrate data in AI/LLM contexts.
• Enterprise system integration experience.
• Hands-on experience with ontology and knowledge graph tools.