AI Software Engineer

At Gradient Labs, we're building the AI customer operations platform for financial services. Founded in 2023, we now work with some of the biggest names in banking and fintech. Our platform runs specialist agents, purpose-built for financial services, to eliminate manual work across customer support and back-office operations. Together, they give product and operations teams the visibility and control to trust every outcome. We're a team of builders from companies like Monzo, Wise, Revolut and Google. If you're excited to tackle some of the hardest problems in AI and help shape the future of customer operations, we'd love to hear from you. 🎯 How you’ll make an impact 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. 💡 What you’ll bring 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.