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machine learning engineer in fintech

Veröffentlicht am

Arbeitsort
10115 Berlin, Berlin, Deutschland

Stellenbeschreibung

Описание

Qonto provides Europe's leading finance workspace for SMEs, with banking at its core and additional financial tools. Its AI team builds customer-facing AI products for business customers in financial services.

Задачи

  • Develop machine learning models end-to-end, from understanding product requirements through training, evaluation, and production deployment
  • Integrate machine learning into the product ecosystem with Product Managers, Data Engineers, and Backend Engineers
  • Build the ML Ops framework, including model drift detection, performance tracking, automated retraining pipelines, monitoring, and alerts
  • Put models into production with robust technical implementation, quality assurance, and continuous monitoring
  • Share best practices and contribute to internal tooling improvements
  • Mentor peers across the ML team

Требования

  • 6+ Years of experience as an ML Engineer with ML Ops experience
  • Experience developing and deploying client-facing ML products end-to-end with measurable impact on real users
  • Experience building and optimising machine learning models for external customers
  • Ability to choose between Generative AI and proven machine-learning techniques
  • Strong Python engineering skills and experience writing resilient, testable code at scale
  • Proficiency with FastAPI or similar frameworks, third-party service integration, and database interaction in production
  • Experience with tools for automated model retraining, performance checking, and drift detection
  • Experience building or significantly improving ML infrastructure
  • Fluent English

Условия

  • Customer-facing AI products used by hundreds of thousands of business customers
  • Modern stack including Python, Snowflake, Kafka, Kibana, PostgreSQL, Airflow, AWS, Prometheus, ArgoCD, GitHub, and Cursor
  • Freedom to test tools that help reach the target
  • Team of 10 AI Engineers and 3 Data Ops specialists
  • Individual contributor career path with access to the latest AI technologies
  • The hiring process lasts 20 working days on average