AI Platform Engineer

Raisin is the world’s leading platform for savings and investment products. Founded in 2012, the FinTech connects consumers with banks in the EU, the UK and the US. This gives consumers better interest rates and banks a diversified form of refinancing. Our vision is to offer savings and investments without barriers and thus open up the global 160 trillion euro market.

Raisin currently employs more than 800 people from over 75 countries worldwide. Today, the platform holds over 80 billion euros in assets from more than one million investors which have accrued over 5 billion euros in returns.

Team

AI Enablement builds Raisin's internal AI platform. We make it easy and safe for every team, from engineering and product to business operations, to use AI in their daily work and in the products they build.

We provide the shared foundation for that: governed access to AI models and tools, the controls that keep data secure and usage auditable, and the visibility teams need to run AI reliably and cost-effectively. We also shape how Raisin builds software with AI, from AI-assisted coding to agentic workflows that support teams across the development lifecycle. Because Raisin operates in regulated financial services, security and compliance are core to how we design the platform.

You will join early and help shape how AI is used across Raisin.

Your Responsibilities

As an AI Platform Engineer, you build and run the platform that lets teams use AI safely, and you help engineering teams bring AI into how they build software. You own your work from the first design to production and work directly with the people who use it. You won't do all of the following at once: you will go deep in a few areas and contribute to the rest.

Build and run the platform

• Design, build, and operate shared services that give teams governed access to AI models and tools, including authentication, routing across providers, usage limits, guardrails, and audit logging.
• Build safe ways for AI agents to connect to internal systems and data, with clear identity, permissions, and review.
• Run what you build in production: infrastructure as code, observability for reliability, usage, and cost, and incident response.

Bring AI into how we build software

• Design and build agentic workflows that support engineers across the development lifecycle, from planning and implementation to code review, testing, and release.
• Create reusable building blocks for AI tools, such as agent instructions, skills, and integrations with engineering systems, that teams can adopt and extend.
• Help engineering teams adopt AI-assisted development in a way that improves quality as well as speed, and measure the effect on delivery.