About the role
Oper, a European Fintech company, is a leader in cloud-based digital mortgage software. We provide personalized journeys for borrowers and lenders. Oper's technology streamlines the mortgage application process, eliminates paperwork, and increases transparency. Currently collaborating with 16 leading financial institutions across 6 European countries, our digital solutions result in a +27% increase in digital customer conversion, +81% faster mortgage decisions, and a 90% first-time-right. Oper's AI Analyst, Herman, is a mortgage underwriting agent that automates document validation, data extraction, fact-checking, and credit policy application to deliver fully explainable, decision-ready loan recommendations in minutes instead of hours. A passionate and engaging team of over 40 stellar Fintech operators works vigorously to transform European mortgages by enhancing efficiency and alleviating borrower anxiety. Backed by Bessemer Venture Partners and Motive Ventures, our platform has raised EUR 14 million, placing Oper at the forefront of revolutionizing mortgages in Europe. Oper is based in Antwerp, Belgium. We are looking for an AI-Native Engineer to join us on a freelance, project-based engagement of around 6 months . The mission is to design and build a production-grade integration layer between Oper's product and a major European customer's banking systems, in the form of a Python microservice. This is a hands-on engineering role for someone who is all-in on AI as the way they build software. We are not hiring for output measured in lines of code; we are hiring for someone who can use modern AI tooling to deliver something close to 5x the value a traditional engineer would, on a real, well-scoped problem. If the engagement goes well and similar projects come up, we are very open to extending or starting new ones together. Tasks Build the integration layer Design and build a Python microservice that acts as the integration layer between Oper's product and the customer's banking systems Define clean API contracts (REST, OpenAPI) on both sides of the integration Implement reliable request handling, retries, idempotency, and error semantics across system boundaries Translate between Oper's domain model and the customer's data formats and protocols Treat the integration as a real product: versioned, documented, and easy to evolve Ship a production-ready service Build the service with FastAPI , async patterns, and strong typing Set up observability end to end: structured logging, tracing, metrics, alerts Handle authentication, secrets, and sensitive data correctly from day one Containerize and deploy via our existing CI/CD setup Write meaningful tests at the right level (unit, integration, contract) Deliver in an AI-native way Use modern AI coding tooling (Cursor, Claude Code, Copilot, or your own setup) as a core part of how you build, not as an afterthought Apply AI to the parts of the work where it actually compounds: scaffolding, adapters from spec, generating tests, reviewing diffs, writing documentation Be honest about where AI helps and where it does not, and design your workflow accordingly Aim for a delivery cadence and output level that a non-AI-native engineer simply cannot match Collaborate and communicate Own the delivery rhythm : weekly demos, written updates, clear progress signals Ask the right clarifying questions early and proactively flag risk Work closely with the Oper engineering, product, and customer-facing teams Leave behind code, documentation, and runbooks that the internal team can confidently maintain after the engagement
