Senior Forward Deployed Engineer
Коротко
Builds dependable production LLM systems: automates business workflows, deploys ML models into customer features, and creates shared tooling. · Needs: London hybrid; strong engineering background with Python; experience taking LLM-based projects to production.
Описание от работодателя
Forward Deployed Engineer (Production LLM Systems)
? London, hybrid
? Permanent
About the Client
Our client is a well established UK business operating in a regulated sector, with a large customer base and a strong engineering culture. They are investing seriously in AI and are building a dedicated engineering team to take it from experiments into systems the business relies on every day.
The Role
Teams across the business have already started building their own AI tools, and this hire will give that work a safe route into production. You will work across three areas:
Partnering with business teams to automate and improve how they work
Taking models built by the in house ML team into live customer features
Turning what you learn into shared tooling the whole company can use
The focus is building dependable software that happens to use LLMs, so you will spend far more time on engineering than on prompts or model training.
What You Will Do
Work directly with business teams to understand their problems, define the right solution, then design, build and deploy it
Take projects from first prototype through to stable production, and agree clear ownership once they are live
Build Python pipelines that combine deterministic steps with LLM calls, using good judgement on where each one belongs
Put evaluation, guardrails, monitoring and cost controls around everything you ship
Rebuild useful tools that teams have created for themselves into properly owned business systems when they prove their worth
Deploy models built by the data science team into customer facing production, covering serving, integration and everything around the model
Spot the patterns across your projects and turn them into shared tools and safe defaults that other teams can pick up and use themselves
Learn from specialist engineers during the early stages of the team and help keep that knowledge in house
What Success Looks Like
Early on, you will pick your first projects with the business, ship them, and be able to point to clear results. You will also help get one of the data science team's models running reliably for customers.
What You Will Bring
Experience building for people outside engineering, whether as a consultant, a forward deployed engineer or on an internal platform team
Comfort taking a vague problem and turning it into a scoped, deliverable system with minimal direction
5+ years of software engineering experience with real ownership of production systems
A track record of building and shipping LLM based systems that have run in production
Strong production Python as your main language
Hands on experience working with models through APIs, including orchestration, structured output, tool use, agents and RAG where it fits
Experience designing evaluation sets, measuring the accuracy of LLM steps, and regression testing prompts and workflows
Practical safety experience, including PII handling, keeping data within the right boundaries, prompt injection awareness and human in the loop design
Experience keeping LLM systems fast and affordable, whether through smarter model selection, caching or batching
CI/CD, containerisation, and monitoring and alerting for AI workloads
Solid data processing fundamentals, including pipelines, transformation, validation and handling anomalies