Machine Learning Engineer

Quick Facts

• Role: Machine Learning Engineer
• Focus: MLOps modernization of actuarial pricing models and an AI platform for document understanding using GenAI

Description

You will help launch an MLOps initiative to modernize actuarial pricing model development by automating model training, deployment, and monitoring for efficiency, reproducibility, and scalability in production. You will also develop an AI-powered GenAI platform to extract, analyze, and categorize information from large document volumes, improving the speed and precision of data retrieval across internal use cases.

Responsibilities

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

Requirements

• Bachelor’s or Master’s degree in Mathematics, Computer Science, Machine Learning, or related field
• Mastery of Data Science frameworks in Python: pandas, pyspark, sklearn, shap
• Mastery of MLOps frameworks: MLFlow, Kedro/Airflow, Hyperopt/Optuna, Great Expectations
• Experience building GenAI agentic workflows with Langchain or smolagents
• Familiarity with dashboarding tools: PowerBI or Tableau
• Strong understanding of DevOps methodologies (CI/CD) and experience implementing GitHub Actions (or similar) workflows
• Experience serving models with APIs using Flask or FastAPI
• Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes)
• Extremely high attention to detail and rigor