Requirements

Expected technologies

Azure

Docker

Kubernetes

MLFlow

Pandas

PowerBI

Langchain

CI/CD

FastAPI

Python

Operating system

Windows

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.

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.