AI Engineer

• You're a generalist by instinct. You've worked in more than one language or platform, and you'd learn another if the job needed it. You don't introduce yourself as a C# or a Java developer.

• You've run what you built. Distributed, event-driven backend systems in production — headless, API-first, where the numbers had to be right and downtime had real consequences.

• You think in systems. Where the coupling sits, what a change is likely to break, how the pieces talk to each other. You will design clear boundaries around complex banking domains, keep API changes backward compatible, and choose simple designs by default. For workflows where reads, writes, and audit requirements differ, you’ll use separate models when the trade-off earns its cost. 

• You're comfortable in code you didn't write. Working out how an existing system actually does, rather than what its documentation claims, is a skill you enjoy using.

• You use AI deliberately. You know how to give a model the context it needs, how to direct it on a large unfamiliar codebase, and judge whether what comes back is any good. You have opinions about where it helps and where it doesn't.

• You own things in production. Cloud, infrastructure as code, CI/CD, and observability are part of your job, not another team's. We run on AWS with Terraform, GitHub Actions, OpenTelemetry, EventBridge, SNS and SQS, but equivalent experience on any major cloud is just as welcome.

• You make your reasoning clear. You can challenge a colleague or client directly and respectfully, explaining the trade-off and proposing a better path.

• Cutting-edge technology in fintech

• Highly qualified colleagues to learn from

• A friendly, multicultural environment where you can learn, grow, and make an impact

• Hybrid working setup with flexible hours

• Work from Anywhere policy

• Language classes and continuous learning and development

• Home office setup support

• Mental health and wellbeing support

• Flexible retribution options (Barcelona)

• Weekly team lunches, wellbeing activities, and regular team events

Do you think like an engineer first, and a language specialist second?

We are looking for engineers who want to build the next generation of cloud-native financial technology.

Our platforms run on.NET and Java, but we are not looking for C# or Java developers (or Python developers, or developers of any other language). We are looking for software engineers, because what matters is that you can reason about distributed systems, become productive quickly in an unfamiliar codebase — including one written in a language you have not used before — and use AI as a real engineering partner rather than an autocomplete tool.

We build a cloud-native core banking platform that safeguards billions of euros of real customers' money. The bar for reliability, scalability, and security is exceptionally high, and we are continuously reinventing how we build software. If you want to help shape the future of engineering rather than watch it happen, we'd love to talk to you.

Ohpen launched the world's first cloud-native core banking platform. We don't just use technology — we build it, and we operate it in production for regulated financial institutions.

You'll join a cross-functional team with end-to-end ownership of a domain: design, build, deploy, operate, support. Not a ticket queue. Deployment is where the value gets delivered, so it isn't a step you hand to another team once the code is written. You'll be accountable for how your services behave in production, not only for how they look at code review.

You'd be working on a platform that has been live and evolving for more than a decade. A lot of the work is understanding, extending, and safely replacing systems that carry real business logic for real clients. The interesting problem here is not writing code quickly — that is now cheap. It is knowing what to change, what it will break, and how to prove it worked.

AI is a first-class tool in that work (expected, not just tolerated). You'll use it to build context on unfamiliar code, explore designs, generate and review implementations, and remove routine work. You stay in the driver's seat: you decide what to accept, what to challenge, and when the model is confidently wrong.