AI Platform Engineer, Agentic Interfaces

Build and operate the interface, semantic layer and controls that let AI agents work inside IFS software safely.

This role is hybrid or remote.

The role:

You will own the web client of an AI agent product for our enterprise customers. It is a React and TypeScript single-page application, and almost everything on its screen arrives as a stream from a language-model agent.

What you will do:

• Build an interface that people can trust while the content on it is still arriving, changing and occasionally wrong.
• Turn what the agent produces into a usable interface: prose, generated views and actions the user can take.
• Make sure the agent acts only with the user's explicit consent, and that people can always see what it did and why.
• Own the front-end architecture, the component system and the contracts with the teams behind the agent.
• Help move the interface towards Lit and Web Components, so that what we build is standards-based and not tied to a single framework or application.
• Treat accessibility, performance, testing and observability as part of the product rather than a late pass.
• Work across the boundary into the services your interface depends on, then ship what you build and run it in production.

Why it is interesting:

Most front-end work assumes that a page loads and is then correct. Here the content arrives in pieces, gets revised, sometimes fails partway and is sometimes wrong. And some of it is code a model wrote, which the browser should not trust. The protocols involved are young and still changing (AG-UI, A2UI, MCP Apps). You will help shape them, not just consume them.

What we are looking for:

• Substantial production single-page applications, built and operated, with real depth in TypeScript and React.
• Asynchronous state you can reason about.
• Browser security as a working concern: iframe sandboxing, postMessage, Content Security Policy, sanitising content you did not write.
• Component and design-system work in a shared codebase, including web components used inside React.
• Accessibility as a practice. You know how to keep it tested.
• Willing to write Go and to read Python. Prior experience in either is not required.
• Test-first habits and continuous delivery.
• Clear writing. Decisions are recorded as short architecture decision records (ADRs), and protocols are documented before they change.
• Comfortable working with AI coding agents as part of how you build.

Also valuable:

• A product surface built on a language model in production: streaming, partial output, retries, and showing uncertainty.
• Hands-on knowledge of the Model Context Protocol (MCP) and MCP Apps, AG-UI or A2UI.
• Experience building with Lit and Web Components, including shadow DOM and custom elements used from other frameworks.
• Expression languages or compilers.
• A platform where behaviour is configured through data or metadata rather than written in code.
• Data visualisation.
• Familiarity with Docker, Kubernetes and Helm.
• Knowledge of an enterprise domain: asset management, field service, manufacturing, supply chain.

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. See the note on language under “Open calls” before advertising.

• 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.

• 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.

• 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.

• Knowledge graphs and semantic modelling. Ontology design, context engineering, embeddings, vector databases, retrieval and search technologies, memory architectures, grounding strategies.

• 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 embrace flexibility and hybrid work opportunities to support diverse needs and lifestyles, while also valuing inclusive workplace experiences. By fostering a sense of community, we drive innovation, strengthen connections, and nurture belonging. Our commitment ensures you can work in a way that suits you best, while also engaging with colleagues to share ideas and build meaningful relationships.