over 5 years ago
Paris, France or Berlin, GermanyStaff+
Responsibilities
- Develop machine-learning models end-to-end, from product requirements through training, evaluation, deployment, and iteration.
- Integrate ML models into Qonto’s financial-services product ecosystem with Product Managers, Data Engineers, and Backend Engineers.
- Build ML Ops infrastructure for model drift detection, performance tracking, automated retraining, monitoring, and alerts.
- Deliver robust, tested production implementations with continuous monitoring and quality assurance.
- Share best practices, improve internal tooling, and mentor peers across the ML team.
Requirements
- 6+ years of experience as a Machine Learning Engineer with ML Ops experience.
- Demonstrated experience developing and deploying client-facing machine-learning products with measurable impact on real users.
- Experience building and optimizing models for external customers, including judgment about when to use generative AI versus established ML techniques.
- Strong Python engineering skills, including resilient and testable code at scale, FastAPI or similar frameworks, third-party service integration, and production database interaction.
- Fluency with tools and practices for automated model retraining, performance checking, and drift detection.
- Fluent English, Qonto’s working language.
Benefits
- Customer-facing AI products used by hundreds of thousands of business customers, with visibility into adoption and other impact metrics.
- Modern flexible stack including Python, Snowflake, Kafka, Kibana, PostgreSQL, Airflow, AWS, Prometheus, ArgoCD, GitHub, and Cursor.
- Individual-contributor growth track with access to current AI technologies.
- Opportunity to join a team of 10 AI Engineers and 3 Data Ops professionals working on AI for financial services.
- Hiring process averages 20 working days.
Tech Stack
Categories
