Machine Learning Engineer

A leading insurance and financial services group is looking for an ML Engineer to join its data and technology organization.

This is an opportunity to work on end-to-end machine learning and ETL processes, supporting advanced pricing, risk, segmentation, and scoring models in a business-critical production environment.

The role is fully on-site and combines machine learning engineering, data pipelines, model deployment, containerization, and close collaboration with DevOps teams.

Responsibilities

• Design, build, and maintain end-to-end ML and ETL processes, including data extraction, data processing, model training, validation, and deployment.
• Own model deployment processes and support production-grade ML environments.
• Build and improve ML pipelines across the full model lifecycle, from experimentation to production.
• Implement containerization using Docker as part of the ML production lifecycle.
• Work with Git-based CI/CD and GitOps processes in close collaboration with DevOps teams.
• Support research, development, and operationalization of pricing, risk, segmentation, behavioral pricing, and scoring models.

Required Qualifications

• Experience in machine learning engineering and end-to-end ML workflows - must.
• Experience designing and maintaining ETL processes -must.
• Experience with data extraction, processing, model training, validation, and deployment - must.
• Experience deploying models into production environments - must.
• Experience with Docker and containerized environments - must.
• Experience working with Git-based CI/CD or GitOps processes - must.
• Ability to collaborate closely with DevOps teams.

CV to: [email protected]