Staff ML Engineer — Production AI for FinTech

Qonto; Paris; Sur place; EUR 120 000 - 180 000; Plein temps

Résumé du poste

Qonto, based in Paris, is looking for a Staff Machine Learning Engineer to build client-facing AI for 600,000+ business customers. You will design, train, and deploy ML models, ensuring production-grade reliability and privacy while collaborating with Product, Data, and Backend teams.

You will implement ML Ops pipelines, monitor performance, and mentor teammates, thriving in a fast-paced fintech environment that values innovation and impact.

Qualifications

• 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.
• Strong Python engineering: You write resilient, testable code at scale. Proficient with FastAPI and production integration.
• ML Ops fluency: Familiar with tools that automate model retraining, performance checking, and drift detection.
• Fluent in English: Qonto's working language.

Responsabilités

• Develop ML models end-to-end: From understanding product requirements to training, evaluating, and deploying models in production.
• Integrate ML into the product ecosystem with Product Managers, Data Engineers, and Backend Engineers.
• Build the ML Ops framework: Create infrastructure for scale, drift detection, monitoring, and automated retraining pipelines.
• Put models into production with rigour: Ensure reliability and privacy in client-facing AI for financial services.
• Raise the bar for the team: Mentor peers and contribute to internal tooling improvements.

Connaissances

ML Engineer

ML Ops

Python

FastAPI

GenAI

Outils

Snowflake

Kafka

Kibana

PostgreSQL

Airflow

AWS

Prometheus

ArgoCD

GitHub

Cursor

Description du poste

Qonto, based in Paris, is looking for a Staff Machine Learning Engineer to build client-facing AI for 600,000+ business customers. You will design, train, and deploy ML models, ensuring production-grade reliability and privacy while collaborating with Product, Data, and Backend teams.

You will implement ML Ops pipelines, monitor performance, and mentor teammates, thriving in a fast-paced fintech environment that values innovation and impact.