Richemont Machine Learning Engineer

Richemont is a Swiss-based luxury goods holding company managing a portfolio of prestigious maisons across jewellery, watchmaking, leather goods and specialist online retail. The group combines heritage craftsmanship with contemporary retail and digital initiatives, supporting brand autonomy within a shared corporate structure.

1. Design, implement and validate production-grade machine learning models to support personalization, forecasting and product insights for Richemont brands.
2. Collaborate with data engineering, product and commercial teams to translate business problems into scalable ML solutions and production data pipelines.
3. Deploy and maintain models in cloud or on-premise environments, establish CI/CD and automated testing for model releases.
4. Implement model monitoring, performance tracking and retraining workflows to ensure robustness and data drift detection.
5. Prototype novel algorithms and proof-of-concepts, then operationalize successful experiments into repeatable components.
6. Document models, APIs and data schemas; mentor junior engineers and contribute to best-practice standards for MLOps and ML governance.

1. Strong software engineering proficiency in Python with solid coding practices and version control (Git).
2. Proven experience deploying ML models to production using containerization and orchestration (Docker, Kubernetes preferred).
3. Hands-on expertise with at least one deep learning framework such as PyTorch or TensorFlow and classical ML libraries (scikit-learn).
4. Practical knowledge of data processing and querying (SQL) and experience with distributed processing tools (Spark) or equivalent.
5. Demonstrable experience in building monitoring, CI/CD pipelines and MLOps tooling (MLflow, Airflow or comparable).

1. Python
2. PyTorch
3. TensorFlow
4. scikit-learn
5. SQL
6. Docker
7. Kubernetes
8. MLflow
9. Apache Airflow
10. Apache Spark
11. AWS
12. GCP
13. Git

Mid-level candidate with practical experience delivering ML systems to production (typical expectation: multiple projects in a production environment). Experience in recommendation systems, personalization, forecasting or computer vision is a strong asset.

Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics or a related field; a Master's or PhD in a quantitative discipline is preferred.

Richemont fosters a culture that balances artisan heritage with digital innovation, encouraging cross-disciplinary collaboration between brand teams and central digital functions. The working environment values meticulous attention to detail, respect for craftsmanship and a pragmatic approach to technology that enhances client experience.