Staff Machine Learning Engineer for AI Engines

Job summary

Responsibilities

Our mission and customers: We are creating the freedom for SMEs to succeed by delivering Europe's leading finance workspace with banking at its core, augmented by financial tools. We are proud to be rated 4.8 on Trustpilot, based on 55,000+ reviews.

• Develop ML models end-to-end: From understanding product requirements to training, evaluating, and deploying models in production.
• 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…
• 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.
• Raise the bar for the team: Share best practices, contribute to internal tooling improvements, and mentor peers across the ML team.

What they'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.
• 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…
• Strong Python engineering: You write resilient, testable code at scale. Proficient with FastAPI (or similar), third-party service integration, and database interaction in…
• 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.

Tech stack

• Python
• FastAPI
• Snowflake
• Kafka
• PostgreSQL
• Airflow
• AWS