Ontology Engineer

Role:
Ontology Engineer

Location:

UK Type:
Contract. Thebes Group is an optimisation company specialising in AI‑enabled transformation. We help organisations improve workflow, reporting, information management, and operational decision‑making by combining process optimisation, knowledge architecture, semantic technologies, automation, and artificial intelligence. We are currently delivering an AI transformation programme for a private equity group, focused on enhancing group‑level operations through intelligent workflows, improved information accessibility, executive reporting, and AI‑driven operational intelligence.

A foundation ontology and taxonomy already exists; the Ontology Engineer will own the meaning layer: defining what data is, how concepts relate, and the rules that govern how information is understood across the organisation.

• Expand and maintain the existing ontology as new business requirements emerge, ensuring consistency with the established semantic model.
• Define and refine classes, subclasses, properties, relationships, and business rules that accurately represent group‑level operational concepts.
• Develop and govern OWL, RDF, RDFS, and SKOS artefacts that form the semantic foundation of the programme.
• Own the enterprise taxonomy, managing it as a versioned, governed asset with clear change control and documented rationale.
• Create and maintain SPARQL queries to validate the integrity and consistency of the ontology.
• Work closely with the Knowledge Graph Architect to ensure semantic models translate correctly into graph structures.
• Collaborate with the AI engineer to review agent outputs, identify where knowledge‑layer gaps are causing errors, and refine the ontology accordingly.
• Establish and maintain semantic standards and governance frameworks covering versioning, change management, and stewardship.
• Document all modelling decisions and change history to support long‑term knowledge asset governance.

• Ontology
  
  Languages:
  
  OWL 2 (DL, EL, RL), RDF/RDFS, SKOS.
• Query & Validation: SPARQL 1.1, SHACL, shape expressions (ShEx).
• Reasoning & Logic:
  Description Logic, OWL reasoning engines (Hermi
  
  T, Pellet, FaCT++), subsumption reasoning, inference rule design.
• Tooling & Platforms:
  Protege, Top Braid Composer, Pool Party, Semaphore, Voc Bench, Apache Jena/Fuseki, Stardog, Graph
  
  DB (Ontotext), Amazon Neptune, Virtuoso, Git‑based ontology versioning, ROBOT, ontology diff tooling, CI/CD for ontology pipelines, change‑log governance.
• Knowledge Architecture:
  Taxonomy design and governance, faceted classification, controlled vocabularies, thesaurus construction, ISO 25964 standards, domain modelling, concept modelling, entity‑relationship design, metadata schema design, linked‑data principles, RAG knowledge‑layer design, knowledge grounding for LLMs, ontology‑driven agent design, Graph
  
  RAG semantic integration, semantic retrieval patterns.

• Essential:
  • Proven experience in ontology engineering with hands‑on OWL, RDF, and SKOS delivery in a production or client‑facing environment.
  • Ability to extend and refine existing ontologies, not just build from scratch.
  • Strong SPARQL capability including validation and reasoning queries.
  • Experience owning and governing taxonomies as versioned, change‑controlled assets.
  • Understanding of how ontologies and taxonomies feed into AI and RAG systems.
  • Strong documentation discipline and ability to record modelling rationale clearly.
  • Experience collaborating with technical teams including data engineers and AI developers.
• Highly Desirable:
  • Experience with Protege, Top Braid, or equivalent ontology tooling.
  • Familiarity with SHACL for constraint validation.
  • Background in financial services, private equity, or similarly structured enterprise environments.
  • Understanding of knowledge grounding principles for large language models.
  • Experience with linked data architectures and triple‑store platforms.

This engagement covers group‑level operations only. Fund management, investment decision‑making, and fund‑level data are explicitly out of scope. The data environment is manageable in scale and well understood within the delivery team. You will work collaboratively with the Knowledge Graph Architect, AI engineer, and wider team to provide context, technical partnership, and support.