Senior Semantic Engineer

Job title: Senior Semantic Engineer

About the Role Senior Semantic Engineer to design and implement semantic data frameworks that provide a shared structure for enterprise data. Focus on building and maintaining ontologies and knowledge graphs, enforcing semantic validation rules for data quality, and integrating semantic structures into AI-powered applications. Industry-agnostic, emphasizing strong semantic web expertise across enterprise contexts.

What You'll Do

• Design, develop, and maintain ontologies (using OWL/RDF or similar), collaborating with domain experts to capture real-world concepts and validate the ontology.
• Build and manage enterprise knowledge graphs, configure graph databases/triple stores, populate RDF triples, and optimize for query performance and scalability.
• Create and optimize SPARQL queries and endpoints to enable efficient retrieval, integration, and analysis of data from the knowledge graph.
• Implement semantic validation rules and data quality checks (SHACL or OWL constraints) to ensure data integrity and quality, defining data modelling conventions and business rules.
• Integrate the ontology and knowledge graph into the organization's data infrastructure and workflows, embedding semantic models in data pipelines, APIs, and databases.
• Collaborate with cross-functional teams (AI/ML, business analysts, data stewards) and communicate semantic concepts to non-technical stakeholders; provide training or documentation.
• Work with AI agents and LLM teams to leverage the ontology and knowledge graph for intelligent applications, enabling context-aware responses and reasoning over the ontologies.
• Stay current with semantic web standards and best practices, contributing to internal guidelines for semantic data management and ensuring a culture of semantically-rich data.

What We're Looking For

• Must have Ontology Design & Maintenance: Design, develop, and maintain ontologies (using OWL/RDF or similar).
• Semantic Web Proficiency: Strong knowledge of OWL, RDF, and SPARQL for ontology modelling and querying.
• Knowledge Graph Experience: Practical experience building or maintaining knowledge graphs or linked data systems in an enterprise setting.
• Data Modelling & Integration Skills: Solid understanding of data modelling, data architecture, and integrating heterogeneous data sources; ability to map relational or NoSQL data to an ontology.
• Programming Skills: Proficiency in Python, Java, or similar.

Nice to Have

• Metadata Standards: Familiarity with metadata standards and vocabularies such as Dublin Core, schema.org, or other industry-specific ontologies/taxonomies. Experience applying these standards to annotate or integrate data.
• AI and LLM Integration: Experience working on projects involving AI agents or large language models where ontologies or knowledge graphs improved AI performance.
• Enterprise System Integration: Proven experience integrating semantic technologies into existing enterprise systems or data platforms.
• Tools & Platforms: Hands-on experience with ontology and knowledge graph tools is beneficial.

Compensation & Benefits

• Salary not disclosed in the posting. The job mentions a question about monthly gross salary expectations.