Machine Learning Engineer

We are launching an MLOps initiative to modernize the development of our actuarial pricing models by integrating best practices in machine learning operations. This project will involve automating model training, deployment, and monitoring processes, ensuring that our actuaries can operate with increased efficiency, reproducibility, and scalability in a production environment.

We are developing an AI-powered platform that leverages GenAI to accurately extract, analyze, and categorize information from large volumes of documents. This platform aims to streamline document processing workflows and enhance the speed and precision of data retrieval across various internal use cases.


Your 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.
  
  
  Our 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