LLM Application Engineer

£90,000 to £115,000
. Permanent. London. Fully remote across the UK.

The job is to make probabilistic models behave like dependable product features. You will own the prompts, context, tools, orchestration and evaluation that sit between an LLM and a user getting something useful done.

Our client is an early-stage AI product company building applications that get on with everyday tasks before you ask. A prototype exists, launch is ahead, and the company is funded without relying on an upcoming round.

You’ll work across model behaviour, backend services and product experience in a small, distributed technical team. A problem is yours from working out what the user needs through to measuring what the system does in production.

The awkward cases are the work. Agents lose context, tools fail, outputs drift and a prompt change can fix one case while breaking another. You need a way to measure that, not just a good demo.

Build LLM-powered applications and multi-step workflows around reasoning, planning, memory and tool use. You'll connect models to APIs, databases, search, internal services and external tools.

Use prompting, context engineering, structured outputs and tool calling to improve how models act. You’ll debug across prompts, orchestration, backend services and product UX when behaviour goes wrong.

Build datasets and evaluation frameworks that measure quality, reliability and regressions. Changes need evidence, especially when model behaviour is non-deterministic.

Optimise for quality, latency and cost, with observability, tracing and experimentation built into the system. The stack includes Python, LLM APIs, open-weight models, vector databases, retrieval systems and PyTorch or JAX.

• AI-powered applications you have shipped to real users.
• LLM or agent systems you have built and run as working software.
• Production Python used to build AI applications.
• Multi-step workflows involving planning, memory or tool calling.
• Evaluation frameworks or datasets you have built for LLM behaviour.
• Retrieval, APIs or external tools integrated into an AI workflow.
• Production AI failures you have investigated across more than one layer.
• Existing right to work in the UK.

Useful: vector databases, RAG, PyTorch, JAX, open-weight models, distributed systems, tracing.

You are probably an LLM Engineer, AI Engineer or Applied AI Engineer who thinks software engineering and evaluation matter as much as model choice. You want to own behaviour after launch, not only get a prototype working.

It won't suit you if you'd rather judge a change by eye. Here every change to model behaviour has to be measured against a dataset you built, and you write the production code around it too.

We welcome applicants from every background and will support reasonable adjustments.