Knowledge Engineer — Knowledge Graph & Agentic Interfaces

Build and curate the ontology, knowledge graph and grounding infrastructure that let AI agents understand IFS software and reason accurately over customer data, turning what the platform already knows about itself, plus deep industry expertise, into a semantic layer other engineers and agents can trust. Key responsibilities - Design and build MCP servers (Model Context Protocol, the emerging standard for exposing application capability to agents) over the product’s business objects, treating capability modelling, discoverability, versioning and backward compatibility as first-class design problems. - Build the write path that lets an agent safely change a customer’s operational data. - Design and build the semantic layer: an ontology and knowledge graph over the product, generated from what the platform already knows about itself and then curated industry by industry, in partnership with domain experts who turn tacit product knowledge into an explicit, machine-usable model. - Build the retrieval and grounding infrastructure that connects agents to this knowledge: embeddings, vector databases, hybrid search, chunking and indexing strategies, memory architectures, and grounding techniques that keep agent outputs accurate and traceable to source. - Establish data quality, provenance and versioning practices for the knowledge graph, so changes are traceable and agent behaviour built on top of it doesn't silently drift. - Build the skills layer that maps what someone asks for onto the correct operation and the correct sequence, with a router that picks between them. - Build the control plane: authentication, entitlements, agent identity, telemetry, metering, resistance to injection, and a default that denies rather than permits. - Build the evaluation harness that certifies agent behaviour against the real product, and improve the system against what it measures. - Build rapid prototypes and proofs of concept to validate emerging technology, product opportunities and customer scenarios. - Establish the engineering practices these systems need: evaluation, testing, observability, monitoring, governance, security and operational excellence. - Contribute to technical design, review other engineers’ work, and support colleagues coming into the domain. - Represent the work outside the team through customer engagements, demonstrations, industry events and partner collaboration. Strong software engineering first. Everything else is applied on top of that. - Production experience building and operating enterprise systems, with real depth in distributed systems, cloud-native architectures, API and schema design, event-driven systems, security, observability and CI/CD. - Strong programming in a modern backend language. - Experience delivering AI systems built on large language models, retrieval-augmented generation (RAG), agentic workflows and orchestration frameworks, including tool use, function calling, workflow orchestration and autonomous or multi-agent architectures, with the judgement to know where they fail. - Deep, hands-on expertise in knowledge graphs and semantic modelling: ontology design (RDF/OWL/SKOS or property-graph equivalents), taxonomy and controlled-vocabulary design, entity resolution, schema evolution and versioning, embeddings, vector databases and grounding strategies. - Evaluation as a discipline: experimentation, benchmarking, prompt engineering, tracing, quality measurement and agent tuning, improving an agent against evidence rather than impression. - Ability to design solutions that integrate enterprise applications, business processes, workflows and data platforms. - Comfort working directly with domain experts to translate tacit business knowledge into explicit, machine-usable models. - Depth in at least one of the following: - Tool-surface and agent-runtime engineering. MCP servers, tool ecosystems, capability modelling, discoverability, g