Original listing text, shown exactly as published by the company.
➡️ What you'll do
- Develop ML models end-to-end: From understanding product requirements to training, evaluating, and deploying models in production. You design, iterate, and ship — not just prototype.
- Integrate ML into the product ecosystem: Align with Product Managers, Data Engineers, and Backend Engineers to ensure your models are seamlessly embedded in Qonto's financial services.
- Build the ML Ops framework: Create the infrastructure for the team to scale — model drift detection, performance tracking, automated retraining pipelines, monitoring, and alerts.
- Put models into production with rigour: Robust technical implementation, quality assurance, and continuous monitoring. Client-facing AI in financial services has no room for silent failures.
- Raise the bar for the team: Share best practices, contribute to internal tooling improvements, and mentor peers across the ML team.
➡️ What we're looking for
- 6+ years as an ML Engineer with ML Ops experience: You've developed and deployed client-facing ML products end-to-end — not internal tools or dashboards. You can show measurable impact on real users.
- Modelling expertise: Experience building and optimising machine learning models for external customers. You know when to use GenAI and when proven ML techniques are the better choice.
- Strong Python engineering: You write resilient, testable code at scale. Proficient with FastAPI (or similar), third-party service integration, and database interaction in production.
- ML Ops fluency: Familiar with tools that automate model retraining, performance checking, and drift detection. You've built or significantly improved ML infrastructure before.
- Fluent in English: Qonto's working language.
➡️ What we can offer you
- Customer-facing AI with real impact: Your models will be used directly by hundreds of thousands of business customers. You'll see adoption metrics, not just offline evaluations.
- A modern, flexible stack: Python, Snowflake, Kafka, Kibana, PostgreSQL, Airflow, AWS, Prometheus, ArgoCD, GitHub, Cursor. You have the freedom to test any tool as long as it helps reach the target.
- A team building AI at the core of fintech: 10 AI Engineers and 3 Data Ops working on innovative solutions at the heart of Qonto's financial services — not a side project.
- Clear IC growth track: Individual contributor career path for those who want to become deep experts in their field, with access to the latest AI technologies.
➡️ Your future manager
Option A
Your manager will be Marianne Borzic Ducournau, Head of Data Products.
- Her background? A graduate of École Polytechnique, Marianne went on to lead Data Science teams at Uber and Amazon in San Francisco before joining Qonto four years ago to build our Data Science team from scratch — hiring the founding members and defining the technical direction.
- What does she bring to the team? A rare combination of applied ML expertise and business context from Finance — she helps people see both the technical and the strategic side of what they're building.
Option B
Your manager will be Benjamin Wolter, Head of AI Products.
- His background? After earning his PhD in Physics and leading ML Engineering and Data Science teams across last-mile logistics and digital marketing, Benjamin joined Qonto to lead our AI Products team.
- What does he bring to the team? Deep technical ML expertise, practical experience building scalable ML systems, and a management style built around ownership and autonomy — he creates the conditions for people to grow without hand-holding.
