Knowledge Engineer — Knowledge Graph & Agentic Interfaces

Overview

Build ontology, knowledge graph, and grounding infrastructure so AI agents can reliably understand IFS software and reason over customer data.

What you'll do

• Design and build MCP servers over product business objects.
• Build a safe write path for agents to change customer operational data.
• Create and curate a semantic layer (ontology and knowledge graph) for the product.
• Implement retrieval and grounding (embeddings, vector databases, hybrid search, chunking/indexing, memory, grounding).
• Establish data quality, provenance, and versioning practices for the knowledge graph.
• Build evaluation, monitoring, governance, and security practices to certify and improve agent behavior.

What you'll need

• Strong software engineering for enterprise systems.
• Experience with distributed systems, cloud-native architectures, API/schema design, event-driven systems, security, observability, and CI/CD.
• Strong backend programming in a modern backend language.
• Experience delivering LLM-based AI systems (RAG, agentic workflows, orchestration, tool use/function calling/multi-agent architectures).
• Hands-on knowledge engineering for knowledge graphs and semantic modelling (ontology/taxonomy/schema evolution, embeddings, vector databases, grounding strategies).
• Evaluation discipline including experimentation, benchmarking, prompt engineering, tracing, quality measurement, and tuning.
• Comfort translating tacit business/domain knowledge into machine-usable models.

Details

• Roles have a right balance of remote and in-office working.