Applied AI Engineer

In this role you will design and build the semantic layer and grounding infrastructure that lets AI agents understand IFS software and reason over customer data. You’ll shape a knowledge graph, MCP servers, and retrieval systems to deliver accurate, traceable agent outputs at enterprise scale. You’ll collaborate with domain experts to convert product knowledge into machine-usable models and establish data quality and governance practices. This is a hands-on role at the forefront of AI-native enterprise software, offering opportunities to prototype, validate, and scale intelligent solutions that impact customers during critical moments.

• Design and implement MCP servers (Model Context Protocol) across product business objects, focusing on capability modelling, discoverability, versioning and backward compatibility
• Develop write paths allowing agents to safely modify customer operational data
• Create the semantic layer: ontology and knowledge graph derived from platform knowledge and domain expertise
• Build retrieval and grounding infrastructure: embeddings, vector databases, hybrid search, chunking, indexing, memory architectures and grounding to keep outputs traceable
• Establish data quality, provenance and versioning for the knowledge graph to prevent drift in agent behavior
• Develop the skills layer to map user requests to correct operations and sequences with routing between them
• Create the control plane: authentication, entitlements, identity, telemetry, metering, security against injections, conservative defaults
• Build the evaluation harness to certify agent behavior and drive system improvement
• Prototype rapidly to validate emerging tech, product opportunities and customer scenarios
• Set engineering practices: evaluation, testing, observability, governance, security and operational excellence
• Contribute to design reviews, mentor colleagues, and represent work to customers, events and partners

• Production experience building and operating enterprise systems with distributed, cloud-native architectures
• Strong backend programming skills in a modern language
• Experience delivering AI systems with large language models, retrieval-augmented generation, agentic workflows, and orchestration frameworks
• Deep expertise in knowledge graphs and semantic modelling (ontology design, taxonomy, embeddings, vector databases, grounding)
• Experience with evaluation disciplines: experimentation, benchmarking, prompt engineering, tracing, measurement, agent tuning
• Ability to design solutions integrating enterprise apps, business processes, workflows and data platforms
• Comfort translating tacit business knowledge with domain experts into machine-usable models
• Depth in at least one area: tool-surface/agent-runtime engineering or enterprise platform depth (e.g., Oracle PL/SQL, OData)
• Desirable: experience with agent frameworks and building reusable AI platforms
• Familiarity with containerised platforms (Docker, Kubernetes) and cloud providers (Azure, AWS, GCP)

• collaboration
• communication with domain experts
• problem-solving
• MCP servers, capability modelling, discoverability, governance, versioning, multi-tenancy isolation
• Knowledge graphs and semantic modelling (RDF/OWL/SKOS or property-graph equivalents)
• Embeddings, vector databases, grounding strategies