Snr AI Engineer
Our client is a digital transformation consultancy. They help large organisations modernise how they operate — better systems, better customer experience, and enterprise AI that actually reaches production.
We are looking for a senior AI Engineer, and it's a hands-on one. You'll design and ship AI-powered products for their customers: agent-based systems, LLM integrations, and the engineering underneath that makes them reliable enough to trust. You'll also be in front of those customers, working out what the real problem is and what's genuinely worth building.
There's a leadership element too. You'd guide a delivery team, set the technical direction on your projects, and bring less experienced engineers along with you.
You'd suit this if you have deep software engineering foundations and have spent the last stretch of your career pointing them at AI — and if you'd rather ship something a customer uses than run another proof of concept.
What you'll do
- Work directly with customers to understand their business problem, then design a build plan that's realistic about risk and timelines
- Design and ship production AI systems end to end — backend, frontend, data and deployment
- Build agent-based systems: orchestration, tool and function calling, memory, retrieval
- Define how you'll know the system works — evals, assertions, quality metrics — and instrument it for tracing and observability, because LLM output won't behave the same way twice
- Work alongside architects, designers and business analysts to land designs that are both usable and buildable
- Lead a delivery team: set direction, mentor engineers, keep quality high through code review and testing
- Use AI-assisted development tooling to move faster, and help the team do the same
What you'll bring
- Around 10 years of software engineering experience, with strong computer science fundamentals — the kind that produce scalable, testable architecture
- Full lifecycle experience across backend and frontend, with depth in Python, Java, React or comparable frameworks
- A track record of building AI-powered products for customers, not internal experiments
- Working knowledge of how agents are actually built — agent frameworks, prompt design, tool calling, orchestration, memory and RAG
- Hands-on experience with frameworks such as LangChain, LangGraph, CrewAI or similar, and the ability to carry those skills onto a cloud agent platform
- Experience evaluating non-deterministic model output and instrumenting systems for observability
- Solid testing discipline — unit, integration and end to end
- Fluent use of AI-assisted coding tools, and a clear sense of where they help and where they don't
- The communication skills to hold a peer-level conversation with a client and explain a technical trade-off to someone who isn't technical
