Knowledge Engineer

Knowledge Engineer focuses on maintain the enterprise taxonomy, ensuring classification remains accurate, consistent and fit for agent consumption.

What the role involves

• Maintain the enterprise taxonomy, ensuring classification remains accurate, consistent and fit for agent consumption.
• Work with the AI engineer to review agent outputs and identify where knowledge-layer gaps or inconsistencies are driving errors.
• Update ontological and taxonomic structures in response to validated agent performance issues.
• Maintaining the knowledge graph as an accurate, traversable semantic layer connecting group operational data.
• Ensure data ingestion into systems is governed by clear metadata and semantic standards.
• Document all structural decisions, changes and rationale to support long-term knowledge asset governance.

Skills and requirements

• Graph databases: Neo4j, Stardog, GraphDB, Amazon Neptune.
• Proven experience in ontology engineering, with demonstrated ability to extend and refine existing models rather than only build from scratch.
• Experience designing or governing enterprise taxonomies, including change management and version control.
• Ability to diagnose agent or system output issues and trace root cause to knowledge structure.

Confirmed role details

• The programme is building AI agents to support day-to-day group operations: workflow, reporting, information management and operational decision-making.
• Govern: manage the ontology and taxonomy as controlled, versioned assets with documented change rationale, ownership.
• Iterate: update ontologies, taxonomies and graph structures based on validation findings, closing the loop between agent performance and knowledge quality.

Candidate fit

• technical judgement, safe working habits, careful diagnostics, and practical problem-solving