Richemont Machine Learning Engineer
- Seniority
- Mid-Level
- Posted
- Mar 20, 2026
About Richemont
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.
Richemont is hiring a Machine Learning Engineer in Moscavide, Portugal to build and deploy production ML solutions for luxury brands.
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.
Workplace
This position is based in Moscavide, Lisbon, Portugal, within easy reach of Lisbon.
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.
About Cerulean
Cerulean is the definitive career portal for the global luxury industry. We match exceptional professionals with exclusive opportunities at the world's most prestigious brands. From haute couture and fine watchmaking to prestige beauty, hospitality, and boutique retail, Cerulean centralises luxury employment to help you find the career for which you were destined.
Frequently Asked Questions
The luxury industry is characterised by a diverse and nuanced nomenclature. Esteemed houses frequently employ proprietary terminology, and even within a single organisation like Richemont, titles may vary across global markets to reflect local conventions. To ensure absolute clarity, Cerulean assigns a standardised, industry-coherent canonical title to every listing. However, it is worth noting that this role is functionally synonymous with «Applied Machine Learning Engineer», «AI Engineer», «MLOps Engineer», «Machine Learning Developer», «Data Scientist — Machine Learning», and other variations. Our sophisticated search architecture anticipates these variations, ensuring that inquiries using related terms will seamlessly yield the exact roles you desire.