AI Platform Engineer, Agentic Interfaces

IFS is building the next generation of AI-native enterprise software for managing assets, operations and critical services. This role offers the opportunity to work at the forefront of modern AI engineering, building intelligent products that combine large language models, agentic AI and cloud-native technologies to solve complex business challenges at enterprise scale. The role requires demonstrable, hands-on experience designing, building and shipping production AI applications.

Build and operate the interface, semantic layer and controls that let AI agents work safely inside IFS software. Key responsibilities include designing and building MCP servers over the product’s business objects, treating capability modelling, discoverability, versioning and backward compatibility as first-class design problems; building the write path that allows an agent to safely change a customer’s operational data; and building a semantic layer comprising an ontology and knowledge graph over the product, generated from the platform’s existing knowledge and curated industry by industry.

You will build the skills layer that maps user requests to the correct operation and sequence, including a router that selects between them. You will also build the control plane covering authentication, entitlements, agent identity, telemetry, metering, resistance to injection and a default-deny security model. Further responsibilities include building an evaluation harness that certifies agent behaviour against the real product, improving the system based on its measurements, developing rapid prototypes and proofs of concept, and establishing engineering practices for evaluation, testing, observability, monitoring, governance, security and operational excellence.

You will contribute to technical design, review other engineers’ work, support colleagues entering the domain, and represent the work through customer engagements, demonstrations, industry events and partner collaboration.

The role requires strong software engineering experience, with production experience building and operating enterprise systems and depth in distributed systems, cloud-native architectures, API and schema design, event-driven systems, security, observability and CI/CD. Strong programming skills in a modern backend language are required. You should have experience delivering AI systems based on large language models, retrieval-augmented generation, agentic workflows and orchestration frameworks, including tool use, function calling, workflow orchestration and autonomous or multi-agent architectures, together with the judgement to understand where these approaches fail.

You should treat evaluation as a discipline, including experimentation, benchmarking, prompt engineering, tracing, quality measurement and agent tuning. You should be able to design solutions integrating enterprise applications, business processes, workflows and data platforms. Depth in at least one of the following is required: tool-surface and agent-runtime engineering, including MCP servers, tool ecosystems, capability modelling, discoverability, governance, versioning, backward compatibility and multi-tenancy isolation; knowledge graphs and semantic modelling, including ontology design, context engineering, embeddings, vector databases, retrieval and search technologies, memory architectures and grounding strategies; or enterprise platform engineering, including Oracle PL/SQL, OData and work with large metadata-driven systems.

Desirable experience includes agent frameworks such as Semantic Kernel, Microsoft Agent Framework, LangGraph, AutoGen, PydanticAI, the OpenAI Agents SDK or CrewAI; building reusable AI platforms, MCP ecosystems or shared engineering capabilities; containerised platforms and infrastructure automation using Docker or Kubernetes; experience with Azure, AWS, GCP or another hyperscale cloud platform; reverse-engineering or interpreter work; token-efficient agent design; enterprise software domains; and contributions to open-source projects, technical communities, conferences, publications or standards.

IFS embraces flexibility and hybrid work opportunities while valuing inclusive workplace experiences and meaningful connections with colleagues.