At a Glance
- Tasks: Own end-to-end product features and deliver impactful solutions.
- Company: Join a fast-moving team revolutionising AI operations for retail and CPG enterprises.
- Benefits: Enjoy an unlimited AI budget, autonomy, and competitive salary with equity options.
- Other info: Dynamic environment focused on learning, iteration, and customer obsession.
- Why this job: Make a real impact by building AI products that solve genuine customer problems.
- Qualifications: Experience in shipping full-stack features and collaborating effectively in teams.
Who we are
Enterprise teams still copy data between systems all day. Work gets stuck in emails, legacy UIs, and handoffs. That chaos is costly, slow, and risky. We're a fast-moving team on a mission to end it for good. Traction is strong and we're solving real problems for real customers, but to win, we need exceptional talent. We stay humble, do the work, and let results speak.
What we are building
We're building the AI operations platform for retail and CPG enterprises—a horizontal platform where AI agents execute end-to-end work across UIs and APIs with governance built in. Where copilots stop, Duvo finishes the job. Business users specify the outcome; agents plan, act, request approvals on exceptions, and learn with every run. We start with a retail wedge (category management, supply chain, finance ops) where ROI is obvious, then expand to adjacent functions and sectors. Velocity is our moat: ship fast, iterate faster, compound learning.
The role
You will own end-to-end product features—from user-facing UI to API to data to deployment. You're the kind of engineer who connects technical choices to customer outcomes and ships with high velocity under ambiguity. Your unit of ownership: user-facing features and the systems behind them, delivered to production and measured against customer impact.
What we're looking for
- Shipping and ownership. You've repeatedly taken ambiguous requirements to production. You own the full stack of a feature (UI, API, data, deployment) and you don't wait for someone to tell you what to build next.
- Product judgment. You can define MVP scope, pick the right metric to move, and kill work that isn't delivering value. You think about what the user needs, not just what's technically interesting.
- AI comfort. You've worked alongside AI systems—at work or in side projects. You're comfortable building features that interact with LLM outputs (parsing agent responses, designing human-in-the-loop flows), but you won't be training models.
- Strong sense of product quality. You care about the details of how a feature looks and feels, not just whether it works. You notice when something is off and you fix it.
- Collaboration. You're low-ego and team-first. You give and receive direct and constructive feedback, align proactively with product and design, and unblock yourself and others.
- Judgment in a fast-moving environment. You'll often define your own scope based on customer problems surfaced through product feedback and competitive gaps — then ship it within a week.
You might also
- Have a strong sense for security and reliability in production systems.
- Have scalable, distributed-system instincts—you've designed and operated systems that scale.
- Have deep applied LLM experience—evaluation design, prompt engineering, safety controls, and cost optimization in production.
- Have experience designing interfaces for AI-assisted workflows — confidence signals, human-in-the-loop interactions.
This is not for you if
- You need significant hand-holding or aren't energized by figuring things out yourself.
- You primarily want to work on infrastructure (see our SRE and AI Platform Engineer roles).
Our tech stack
TypeScript-first: Next.js, React, Tailwind, Fastify, Kysely (PostgreSQL), ZodGCPLatest AI primitives. You don't need to know all of these, but you should be excited to learn them quickly.
How we work
- Initiative-driven. We organise around customer problems, not org charts. Problems surface through product feedback, competitive analysis, and direct customer conversations — then we prioritise, build, and ship weekly.
- Customer-obsessed. We solve real problems, not hypothetical ones. Features that don't move customer metrics get cut.
- Iterative by default. We ship small, learn fast, and never get attached to yesterday's code. This means things break sometimes — we fix forward.
- AI-first leverage. We use AI to move faster and focus human time where it matters most. If a tool can do it, a person shouldn't.
- Direct feedback. We give each other actionable feedback immediately. This can feel uncomfortable — we think that's worth it.
- Autonomy with accountability. We trust people to make decisions and hold them to outcomes, not process.
What we offer
- Unlimited AI budget. We don't just allow AI tools — we strongly encourage them. Want to try a new tool? Buy it. Want to automate part of your workflow? Do it.
- Autonomy to do your best work. Want to meet someone to learn from? Set it up. Want a mentor? Go get one. Want to fly out to talk to an important customer? Just ask.
- A real AI product with real customers. You're not building demos or internal tools. Enterprise customers use what you ship, and their feedback drives what you build next.
- A sharp, motivated team that values ownership and candour.
Compensation 250.000,- CZK / month with a meaningful equity component. You can trade salary for additional equity if you prefer more upside.
How we hire
We respect your time and aim to move fast. Hiring manager screen (30 min). We'll talk about you, how you think and whether there's mutual fit. Remote task (async, time-boxed, ~1 hour). Build a small product end-to-end. Not LeetCode. Technical interview (Prague, ~1 hour). Meet the team. We'll go deeper on your experience, system design, product thinking, and collaboration. No trick questions — we want to see how you think and build. On-site trial day (2 days). Ship something small to production with us and see how we work together. Fully compensated.
AI Product Engineer - End-to-End Ownership in London employer: duvo.ai
At Duvo, we pride ourselves on being an exceptional employer that fosters a culture of autonomy, innovation, and collaboration. Our team is driven by a shared mission to solve real problems for our enterprise customers, offering unlimited AI budgets and opportunities for personal growth in a fast-paced environment. With a focus on meaningful work and direct feedback, we empower our employees to take ownership of their projects and make a tangible impact in the retail and CPG sectors.
StudySmarter Expert Advice🤫
We think this is how you could land AI Product Engineer - End-to-End Ownership in London
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at duvo.ai or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to duvo.ai.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like duvo.ai.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like duvo.ai that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace AI Product Engineer - End-to-End Ownership in London
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at duvo.ai.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at duvo.ai and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at duvo.ai
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If duvo.ai uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.