Qonto Berlin

machine learning engineer in fintech

Gehalt
75.000–110.000 € brutto/Jahr
Arbeitsort
Berlin, Berlin, Deutschland
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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

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