Knowledge Engineer — Knowledge Graph & Agentic Interfaces

Job description

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, governance, versioning, backward compatibility, multi-tenancy isolation. Enterprise platform depth. Oracle PL/SQL, OData, and comfort working inside large metadata-driven systems where behaviour is configured rather than coded. Desirable Experience with agent frameworks such as Semantic Kernel, Microsoft Agent Framework, LangGraph, AutoGen, PydanticAI, the OpenAI Agents SDK or CrewAI. Experience building reusable AI platforms, MCP ecosystems or shared engineering capabilities used across multiple products and teams. Containerised platforms and infrastructure automation: Docker, Kubernetes. Experience with Azure, AWS, GCP or another hyperscale cloud platform. Reverse-engineering or interpreter work. Token-efficient agent design. Enterprise software domains: enterprise asset management, service management, manufacturing, supply chain, aerospace and defence, energy, telecommunications, construction, industrial AI. Contributions to open-source projects, technical communities, conferences, publications or standards. We believe that coming together as a community, in person, is important for innovation, connection and fostering a sense of belonging. 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. 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. IFS is a billion-dollar revenue company with 6000+ employees on all continents. Our leading AI technology is the backbone of our award-winning enterprise software solutions, enabling our customers to be their best when it really matters–at the Moment of Service™. Our commitment to internal AI adoption has allowed us to stay at the forefront of technological advancements, ensuring our colleagues can unlock their creativity and productivity, and our solutions are always cutting-edge. At IFS, we’re flexible, we’re innovative, and we’re focused not only on how we can engage with our customers but on how we can make a real change and have a worldwide impact. We help solve some of society’s greatest challenges, fostering a better future through our agility, collaboration, and trust. We celebrate diversity and understand our responsibility to reflect the diverse world we work in. We are committed to promoting an inclusive workforce that fully represents the many different cultures, backgrounds, and viewpoints of our customers, our partners, and our communities. As a truly international company serving people from around the globe, we realize that our success is tantamount to the respect we have for those different points of view. By joining our team, you will have the opportunity to be part of a global, diverse environment; you will be joining a winning team with a commitment to sustainability; and a company where we get things done so that you can make a positive impact on the world. We’re looking for innovative and original thinkers to work in an environment where you can #MakeYourMoment so that we can help others make theirs. With the power of our AI-driven solutions, we empower our team to change the status quo and make a real difference. If you want to change the status quo, we’ll help you make your moment. Join Team Purple. Join IFS.