MLOps Engineer

Responsibilities:

• Create continuous integration templates tailored for model development ensuring version control, testing, and reproducibility of our actuarial pricing models and datasets
• Close work with members of the ML Engineering team and actuaries to audit and optimize the reliability and scalability of the actuaries' model training pipelines
• Develop effective monitoring strategies to track the performance, reliability, and efficiency of the system
• Manage the end-to-end operation of the AI platform to guarantee high availability, responsive performance, and secure data handling during document ingestion and processing
• Oversee the integration and management of cloud resources to optimize cost, performance, and compliance with security standards, thereby enabling continuous innovation on the platform

Requirements:

• Bachelor's or Master's degree in Mathematics, Computer Science, Machine Learning, or related field
• Mastery over Data Science frameworks (pandas, pyspark, sklearn and shap) and MLOPS frameworks (MLFlow, Kedro/Airflow, Hyperopt/Optuna and Great Expectations) in Python
• Experience with building GenAI agentic workflows using Langchain or smolagents
• Basic familiarity with Dashboarding tools (PowerBI/Tableau)
• Strong understanding of DevOps methodologies (CI/CD) and experience implementing Github Actions (or similar) workflows
• Experience with serving models with APIs using Flask or FastAPI
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization (e.g., Docker, Kubernetes)
• Extremely high attention to detail and rigor
• English - at least B2 level

Nice to have:

• French - A2/B1

Offer:

• Private medical care
• Co-financing for the sports card
• Constant support of dedicated consultant
• Employee referral program