Knowledge Officer

We are engaged on an AI transformation programme for a private equity group, focused exclusively on group-level operations, not fund management or investment activity. The programme is building AI agents to support day-to-day group operations: workflow, reporting, information management and operational decision-making. A foundation ontology and taxonomy already exists, and the organisation's data is mapped and manageable in scope.

The Semantic Knowledge Architect joins an established delivery team that includes an AI engineer responsible for agent development. The two roles work in close partnership: the AI engineer builds and maintains the agents; your responsibility is the quality, integrity and evolution of the semantic knowledge layer: the ontologies, taxonomies and knowledge graph structures that determine what agents know, how they connect information, and whether their outputs are accurate. The accuracy of agent outputs across the group depends directly on the quality of the knowledge layer you maintain.

• Govern: manage the ontology and taxonomy as controlled, versioned assets with documented change rationale, ownership and review cycles.
• Validate: review agent outputs in collaboration with the AI engineer, identify gaps or inaccuracies that originate in the knowledge layer, and trace them to their structural source.
• Iterate: update ontologies, taxonomies and graph structures based on validation findings, closing the loop between agent performance and knowledge quality.

You will work closely with other team members for data context and domain knowledge. The data landscape is already understood within the team, so onboarding to the knowledge environment will be well supported.

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. Maintain 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. Contribute to the broader delivery team, sharing knowledge context with data, platform and business colleagues as needed.

The role operates across four connected disciplines that together form the full knowledge governance and agent quality chain:

• Build upon and extend the existing ontology foundation. The core domain model exists and the data is mapped. Version control for ontology assets.
• Own and govern the taxonomy as a living asset. Change management for knowledge assets.
• Metadata standards for data ingestion alignment.
• Maintain and extend the knowledge graph as the operational semantic layer connecting data sources and AI agents. Ensure it remains accurate, consistent and fit for agent consumption as requirements evolve.

Graph data modelling, graph databases, data integration and linkage, and semantic integration models connecting data sources accurately are essential. Work alongside the AI engineer responsible for agent development to review agent outputs, identify where outputs are inaccurate or incomplete, and trace issues back to the knowledge layer. Conduct root cause analysis within knowledge structures and document root cause analysis for knowledge-layer failures. Implement iterative ontology and taxonomy updates driven by agent performance and establish a shared feedback process with the AI engineer covering knowledge quality.

Experience with knowledge graph design and implementation using Neo4j, Stardog, GraphDB or Amazon Neptune is required. Familiarity with RAG architecture, GraphRAG or semantic retrieval as it relates to agent knowledge quality is beneficial. Exposure to financial services, private equity operations or similarly structured enterprise environments is advantageous. Understanding of how AI agents consume ontologies and taxonomies and where structural gaps create output failures is crucial.

Fund management, investment decision-making, portfolio company activity and fund-level data are explicitly out of scope. The data environment is manageable in scale and already understood within the team. This is not a role that requires building a knowledge strategy from zero. We work with regulated industries and complex enterprises to reduce operational risk and build the foundations for intelligent transformation. This role offers the opportunity to do technically serious knowledge engineering work inside a live AI transformation programme, with a clear scope, a supportive team and direct accountability for the quality of what gets built.