> Markdown version of [/jobs/ext/3293165-senior-ai-engineer](https://www.wearedevelopers.com/jobs/ext/3293165-senior-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Senior AI Engineer

- **Company:** Gradient Labs
- **Location:** London, UK
- **Experience:** Expert
- **Salary:** £89,248.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Machine Learning, Software Engineering, Large Language Models, AI Platforms
- **Published:** September 1, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5869165477

## About the Role

* Professional software engineering experience, with a meaningful focus on Machine Learning, NLP, or applied AI. At this time, we need the ML/applied AI experience as a non-negotiable requirement for this role.
 * Experience shipping products to real, live customers, not just internal tools or prototypes, ideally at meaningful scale.
 * A strong product mindset - you can take a vague problem, break it down from first principles, and know how to get to a valuable first version. You have a preference for fast iteration over long research cycles.
 * Hands-on experience building with LLMs, whether in a previous role, at a startup (even one that didn't work out), or on a small, scrappy team.
 * Comfort with ambiguity, and the confidence to say "I don't understand" and work through it rather than guessing. You can take open-ended problems (like "help our agent handle conversations in multiple languages") and turn them into scoped, shippable projects.
 * Strong communication skills - you can explain the reasoning and trade-offs behind your decisions, not just describe what you built, you communicate clearly and often, and you flag early when you're stuck.
 * A pragmatic, tech-agnostic approach - no specific tech stack required, just good judgement about what's right for the problem.

## Description

This is a build-and-ship role. You'll turn ambiguous customer support problems into reliable, observable AI agents that handle live conversations for real users. You'll work close to production - designing prompts and tool flows, building eval suites, shipping changes, watching what breaks, and iterating fast.

 * Build and operate AI agents in production: Design, implement, and maintain agentic systems powered by LLMs - handling tool calling, multi-step reasoning, and integration with customer APIs and data sources. You'll own these systems end-to-end: reliable, observable, and auditable from day one.
 * Translate business problems into agentic workflows: Take on ambiguous, open-ended problems (like "help our agent handle conversations in multiple languages") and turn them into scoped, shippable projects.
 * Strong product mindset: Prioritise product thinking over pure ML technique, optimising for customer and business value rather than model performance for its own sake.
 * Build robust evaluation infrastructure: Create and maintain eval suites drawn from real-world scenarios and edge cases. Go beyond vibes-based testing: structured evals measuring accuracy, safety, and latency, tied to clear business outcomes, used to drive systematic improvements to prompts, tools, and behaviour.
 * Enhance our agent: Develop, evaluate, and optimise the skills that make up our agent. Curate datasets, iterate on improvements, test changes, and ship successful approaches into production.
 * Shape our internal AI platform: Contribute to shared libraries, patterns, and standards for how we build, evaluate, and deploy agents across customers. Help define how we approach prompting, tool orchestration, retrieval, and monitoring.
 * Experiment and prototype: Keep up with the latest in NLP, agentic systems, and generative AI. Prototype against our hardest problems with a bias toward shipping experiments quickly rather than long research cycles. Our agents already handle tens of thousands of real customer conversations every hour, so your work has immediate, visible impact.
 * Analyse data: Work across customer queries, support tickets, and related data to find patterns and identify what our agents could automate next.
 * Drive technical decisions: Scope your own work, push back when the framing is wrong, and tell us when the plan needs to change., * 45 mins First Stage interview with one of our AI Engineers to talk through a complex AI project you've worked on
 * Take Home Task
 * 1hr Mid-stage Interview to discuss the take home task you've completed with our Chief Scientist
 * 1hr Final Interview focusing on product thinking