AI Knowledge Enginner
AI Knowledge Engineer
- Hybrid- Remote
- 6 months +
- Inside IR35
- SC Clearance (or eligible)
Our customer, a central government organization, are building an enterprise-grade AI platform to enable secure, scalable and production-ready use of Generative AI and ML across the programme.
The successful candidate will be responsible for:
- Building and populating knowledge graphs programmatically from structured and unstructured sources (not UI-based curation)
- Graph databases/query (eg Neo4j/Cypher or RDF/SPARQL)
- Python data engineering for ingestion/ETL at volume
- Vector embeddings and chunking strategy
Nice to have:
- LangGraph or equivalent orchestration experience
- AWS-native AI stack (Bedrock, OpenSearch); other cloud stacks considered if transferable
- Regulated/government environment experience
- The Knowledge Engineer builds and operates the pipelines that populate and maintain the CDS knowledge graph at volume, turning the Knowledge Modeller's schema and platform-team retrieval design into a working, governed data asset.
Key Responsibilities:
- Build ingestion pipelines to populate the knowledge graph programmatically from structured and unstructured sources
- Implement and maintain graph population workflows (entity/relationship extraction, loading, validation against the defined schema)
- Own chunking and embedding strategy for content feeding retrieval
- Monitor and maintain data quality, freshness and lineage within the knowledge graph
- Support reconciliation processes to catch ingestion gaps (eg missed webhook events) without adding load on source systems
- Work with AI Engineers to ensure the graph and retrieval layer serve RAG/agent consumption correctly
- Contribute to evaluation of graph/retrieval quality from a data-completeness perspective (distinct from AI Engineer's model-output evaluation)