Machine Learning Engineer

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.

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.

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