Forward Deployed Engineer

Embed with customers to understand their actual operation — their data, their constraints, their people — rather than working from a requirements document written by someone who's never been on site

Lead technical discovery: separate the real bottleneck from the symptom the customer came to you with

Design and ship full-lifecycle features across front-end and back-end, and build or extend the APIs and event-driven services that connect workforce, project, scheduling and financial data across IFS Cloud and customer systems

Know the platform and AI service catalogue well enough to spot what can be reused or extended, and help customers build on top of it

Where nothing fits, gather the requirements yourself and partner with the owning engineering team to scope and build it — hands-on throughout, not handing off a ticket

Get into the data pipelines, model serving, retrieval and evaluation behind AI-backed features, and the integrations that put them into a real workflow

Ship a working first version fast, then harden it: monitoring, feedback loops from real usage, production-grade reliability

Decide what survives. Some of what you build proves a point and gets deleted; some becomes product for every customer. Calling that correctly, and telling a customer no, is part of the job

Communicate trade-offs clearly to product owners, customer architects and non-technical stakeholders, and turn what you learn in the field into input for the roadmap

You see the consequences.

A new problem every few months.

AI-native, for real.

Full ownership.

A real path back into product.

Attitude Speed and attitude first. You thrive in ambiguity — you can turn "this process feels like it needs AI" into a scoped, shipped system without waiting to be told exactly what to build. Blocked, you find the way through, escalate early, or change the approach. You don't wait. Customers trust you quickly because you're straight with them. You can talk to an architect and a shop-floor supervisor on the same day and be useful to both, without dumbing down the substance. The bar

5+ years building and operating production systems, with the ability to work across an unfamiliar stack or customer codebase

Languages: strong in at least one modern general-purpose language, Python included or picked up fast

Cloud and Kubernetes: building and operating cloud-native services. Azure — AKS, Blob Storage, Key Vault, Azure-hosted AI services — is highly relevant

AI and LLM integration: LLMs in production applications — model APIs, auth, gateways, reliability, latency, cost, observability

Agentic systems: agentic applications using tools, APIs and orchestration frameworks. Hands-on MCP strongly preferred

Retrieval and RAG: production retrieval systems — embeddings, vector stores, indexing, retrieval quality, evaluation

APIs and enterprise integration: REST, auth/authz, data contracts, and the patience to debug someone else's system

Deployment and IaC: Docker, Kubernetes, Helm, GitOps/ArgoCD, Terraform or equivalent

Data: pipelines and comfort across relational, document, vector and object stores

Pragmatic grasp of event-driven and distributed systems, and of AI failure modes and the quality/latency/reliability/cost trade-offs

Genuinely fluent with agentic tooling in daily engineering work, not just aware of it

Enterprise or ERP software. Project- or asset-heavy industries. Consulting or solution engineering. Experience working alongside platform or infrastructure teams. Agile delivery with globally distributed teams.

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.