Machine Learning Engineer

Quick Facts

• Role: Machine Learning Engineer (MLOps & GenAI platform)

Description

Help launch an MLOps initiative to modernize actuarial pricing model development by automating training, deployment, and monitoring for production use. You will also develop an AI-powered GenAI platform that extracts, analyzes, and categorizes information from large volumes of documents to improve document processing and data retrieval. The work emphasizes efficiency, reproducibility, scalability, and secure operations.

Responsibilities

• Create continuous integration templates for model development to ensure version control, testing, and reproducibility of actuarial pricing models and datasets.

• Work closely with ML Engineers and actuaries to audit and optimize the reliability and scalability of training pipelines.

• Design monitoring strategies to track performance, reliability, and efficiency.

• Run the AI platform end-to-end to maintain high availability, responsive performance, and secure data handling during document ingestion and processing.

• Integrate and manage cloud resources to optimize cost, performance, and compliance with security standards.

Requirements

• Bachelor's or Master's degree in Mathematics, Computer Science, Machine Learning, or a related field.

• Expertise in data science frameworks (pandas, pyspark, sklearn, shap) and MLOps frameworks (MLflow, Kedro/Airflow, Hyperopt/Optuna, Great Expectations) in Python.

• Experience building GenAI agentic workflows using LangChain or smolagents.

• Basic familiarity with dashboarding tools (PowerBI/Tableau).

• Strong DevOps understanding (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.