Knowledge Engineer — Knowledge Graph & Agentic Interfaces Staines-upon-Thames, England, United Kingdom Research and Development

At IFS, we're building the next generation of AI-native enterprise software, transforming how some of the world's largest organisations manage assets, operations and critical services. This is an opportunity to work at the forefront of modern AI engineering, building intelligent products that combine Large Language Models (LLMs), agentic AI and cloud-native technologies to solve complex, real-world business challenges at enterprise scale.

We're looking for engineers who are passionate about building production AI systems and excited by the opportunity to shape the future of enterprise software. Please note that this role requires demonstrable, hands-on experience designing, building and shipping production AI applications.

Key responsibilities:

• Design and build MCP servers (Model Context Protocol) 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.
• 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.
• Establish data quality, provenance and versioning practices for the knowledge graph.
• Build the skills layer that maps what someone asks for onto the correct operation and the correct sequence.
• Build the control plane: authentication, entitlements, agent identity, telemetry, metering, resistance to injection.
• Build the evaluation harness that certifies agent behaviour against the real product.
• 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.

Deep, hands-on expertise in knowledge graphs and semantic modelling: ontology design, 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.

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, enterprise platform depth, experience with agent frameworks, experience building reusable AI platforms, containerised platforms and infrastructure automation.

Experience with Azure, AWS, GCP or another hyperscale cloud platform. Contributions to open-source projects, technical communities, conferences, publications or standards.

Our roles have the right balance of remote and in-office working to enable flexibility for managing your life along with ensuring a real connection with your colleagues and the broader IFS community.