AI Product Builder
- Hybrid
- All ai & machine learning jobs
- Employee - Permanent
- AI Banking
About the role
At Zopa, we're building AI Banking: an intelligent, conversational experience that lets customers do almost anything with their money just by asking. Move money, split a bill from a photo of a receipt, find a payment from last year, understand where their money went, freeze a card or set a savings goal. It is live to customers today and on its way to becoming the primary way people interact with Zopa.
Underneath it sits a platform we have built ourselves: an agentic loop, MCP-based tools, memory, a generic front end and an evals framework. Because that platform exists, the work has changed shape. A new customer experience is often a prompt change, a tool definition and a set of evals, not a quarter of engineering. One person with good judgement can now design, build and ship an entire experience.
That has created a role that does not really exist yet in most companies and sits between product and engineering. As an AI Product Builder, you will own a customer problem end to end and build the answer yourself on our platform, with AI doing most of the typing.We are a small team with a lot of autonomy, shipping fast in one of the most heavily regulated industries there is. If the idea of having your own agentic banking experience in front of hundreds of thousands of customers within weeks appeals to you, read on.
A day in the life:
- Own a customer problem end to end: identify the opportunity, decide what should exist, build it, ship it and learn from what happens next.
- Prototype your own ideas rather than writing a document for someone else to build. Here, we often build before we debate.
- Create complete customer experiences on our agentic platform, often through prompts, tools and configuration rather than new services.
- Design the evals that define what good looks like for your experience, using them to inform iteration and as a key part of the release decision.
- Work directly in the codebase, raise your own PRs for engineering review and partner with engineers when a problem needs deeper technical work.
- Shape how your agent performs in production, balancing context, cost per conversation, latency, model choice and routing.
- Review real customer conversations and turn what you learn into meaningful improvements, often within days rather than weeks.
- Lead your own analysis and research: interrogate the data, run the numbers, review transcripts and build the insight needed to make a decision.
- Make sound judgements on risk, compliance and potential customer harm as you build, working closely with Risk and Compliance partners.
About you:
- You are a builder. You can point to customer experiences you have personally built and shipped using AI, and clearly explain the decisions you made along the way.
- You have strong product taste. You know what good looks like, and just as importantly, what is not ready to ship.
- You can uncover meaningful customer insights and turn them into experiences that solve real problems in new and intuitive ways.
- You use coding agents regularly and have a thoughtful view on where they add value, where they fall short and how to use them well.
- You have designed evals and can explain how they helped you identify issues that traditional testing alone would have missed.
- You understand how LLM systems work in practice, including context management, tool design, prompting, non-determinism, and trade-offs around cost and latency.
- You think in systems. When you encounter a recurring problem, you look for a way to solve the underlying cause, not just the individual symptom.
- You are deeply curious, open-minded and comfortable changing your approach as the technology evolves.
You’ll likely bring many of the following:
Added bonus
- Banking, fintech, payments or another regulated environment.
- Building and operating agentic systems in production, with real users and meaningful consequences.
- Model routing, context engineering or cost optimisation at scale.
- Voice, image-based or other multimodal customer experiences.
- Side projects, open-source contributions or things you have built simply because you wanted to.
What this role is not:
- This is not a traditional product role where you write requirements and hand them to a delivery team. You will be expected to build, test and ship customer experiences yourself.
- This is not a people-management or large-scale programme-management role. The model is small teams with high autonomy and outsized impact.
- Research is an important part of the work, but it is always in service of getting something valuable into customers’ hands quickly.
- Your progression will be based on the quality and impact of what you create, rather than team size, reporting lines or the breadth of your remit.
At Zopa we value flexible ways of working.
We value face-to-face collaboration and a good work-life balance. This hybrid role requires you to come to our London office 2-3 days a week.
You'll also have the option of working from abroad for up to 120 days a year!* But no matter where you are, we’ll make sure you’ve got everything you need to thrive, both in your work and home life, from day one.
*Subject to having the right to work in the country of choice
Diversity Statement
Zopa is proud to offer a workplace free from discrimination. Diversity of experience, perspectives, and backgrounds leads to better products for our customers and a unique company culture for our people. We are made up of nearly 50 nationalities, have a DE&I forum made up of Zopians wanting to make a difference and we are proud of our culture where everyone can bring their full self to work. Our approach to DE&I is reflected in our hiring process so please let us know if you require any reasonable adjustments.
Our approach to AI in interviews
At Zopa, AI isn't something we're testing out — it's part of how we work every day. As a proud partner of Jobs 2030, we're committed to building AI fluency across our workforce, and we expect Zopians to use AI as part of how they do their jobs.
Because of that, we want to be transparent about how we think about AI use during our hiring process.
Behavioural and competency-based interviews: please don't use AI. These conversations are designed to understand you — your experiences, your judgment, and how you've approached real situations. An AI-generated answer can't tell us that. What it can do is get in the way of us finding out whether we're the right fit for each other.
Technical interviews: it depends on the role. Some technical stages actively welcome AI use, others don't. Your Talent Partner will let you know what's expected at each stage. Where AI is part of the assessment, we'll be interested not just in the outcome, but in how you used it – the tools you chose, your reasoning, and the decisions you made along the way.