27 - AI Native Full Stack Software Engineer

Role Overview
Our client has been building online accounting software for over 20 years , tools that help entrepreneurs and accountants run their administration smarter and more efficiently. They're part of a large European software group, which means you get the agility of a focused product team backed by the resources and structure of a major international player.

As a Full Stack Software Engineer, you're not just writing code , you're translating business challenges into technical solutions, shipping new features, and actively contributing to how the product evolves. You'll have room to take initiative, pitch ideas, and shape direction both technically and strategically.

What You'll Be Doing

• Designing, developing, and maintaining Java-based projects

• Optimising existing features with scalability and usability in mind

• Translating business requirements into efficient, impactful solutions

• Fixing bugs and structurally preventing them from recurring

• Refactoring legacy code and systematically reducing technical debt

• Exploring and applying new technologies and best practices

What We're Looking For

• Bachelor's or Master's in IT, or equivalent experience — with at least 5 years in software development

• Strong engineering fundamentals: clean code, testing, version control

• Hands-on experience with Java/Spring, testing frameworks (JUnit, Playwright), and SQL databases

• An AI-native mindset: you use tools like Claude Code, Cursor, or Copilot daily, and know when AI helps and when it doesn't

• Strong analytical and communication skills — you think along, speak up, and challenge when needed

• Experience with frontend technologies

• Experience with cloud platforms (AWS)

ATTENTION UPON DROPPING YOUR APPLICATION:

Please, immediately send an email with subject 'AI Native Full Stack Engineer – Your Name' sharing concrete examples of Java/Spring backend work you've done in production.

We're specifically interested in:

• Java/Spring services you've designed or maintained in production — the scale, key architecture decisions, and how you approached testing (JUnit, Playwright)

• SQL database work you've owned — schema design, query optimisation, or migrations

• Legacy code you refactored or technical debt you paid down — what was wrong, what you changed, and the outcome

• How you use AI coding tools (Claude Code, Cursor, Copilot) day-to-day — a specific example where AI sped things up, and one where you deliberately didn't use it

• Frontend or AWS/cloud experience, if you have it — what you built and your role in it

Please point us to production work, not tutorials, side experiments, or course projects. A short paragraph per example is enough. Vague answers like "I use Copilot for most of my coding now" won't move you to the next stage, specifics will.