Machine Learning Engineer

Overview

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.

Role & Responsibilities

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

Qualifications

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

Skills

Experience

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.

Education

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

Culture

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.