ML Ops Engineer

Join our team as an ML Ops Engineer, where you will be responsible for the reliability, scalability, and operational integrity of our machine-learning systems. This hands-on engineering role will involve designing and operating data pipelines, building tooling for training and deploying models, and collaborating with research engineers and product teams. You will also be responsible for productionizing models, implementing model monitoring, managing experiment tracking, and ensuring data quality.

• Assurer la fiabilité, l'évolutivité et l'intégrité opérationnelle des systèmes d'apprentissage automatique en recherche et en production.
• Concevoir et exploiter des pipelines de données qui alimentent les modèles, et construire les outils qui entraînent, déploient, surveillent et réentraînent les modèles.
• Collaborer avec les ingénieurs de recherche et les équipes produit pour passer des modèles de l'expérimentation à des systèmes de production de qualité.
• 3–7 years of professional experience in ML Ops, Data Engineering, or adjacent backend roles
• Strong production Python skills (clean APIs, testing, performance awareness)
• Experience deploying and operating ML models in production environments
• Failure modes in data-driven systems
• Batch vs. streaming data pipelines
• Comfort working with cloud infrastructure and containerized workloads
• Ability to reason about system design, not just tool usage
• Solid understanding of:
• Hands-on experience with at least one modern orchestration or workflow system
• Model training vs. inference requirements
• Experience operating systems at TB-scale data volumes or higher
• Prior ownership of model monitoring, drift detection, or automated retraining
• Familiarity with feature stores or online/offline feature consistency problems
• Experience supporting multiple models or teams on a shared ML platform
• Exposure to regulated or high-reliability production environments