DESCRIPTION

The Machine Learning Operations Engineer will support the development and maintenance of the ML Ops platform for Project Pegasus. This role involves building and maintaining the data infrastructure required for the platform, developing API services, and ensuring the integration of ML models into the live environment.

ABOUT SOMERSET BRIDGE GROUP

Somerset Bridge Group is dedicated to delivering fair products and innovative services in the insurance industry. Our group focuses on underwriting, broking, and claims handling to provide sustainable and innovative insurance solutions. Somerset Bridge Insurance Services Limited, operating under GoSkippy and Vavista, offers insurance coverage to over 700,000 customers. Somerset Bridge Limited handles underwriting and claims, processing almost 50,000 claims annually. Somerset Bridge Shared Services Limited provides essential support functions to ensure operational efficiency and compliance. With a strong commitment to values, culture, and customer service excellence, Somerset Bridge Group is recognised for its industry awards and growth. Join us to be part of a dynamic team that fosters creative thinking and personal development.
We are very proud to have been awarded a Silver Accreditation from Investors in People! We recognise that all of our people contribute to our success. That’s why we are always looking for talented people to join our team - people who share our vision, who are passionate about what they do, and who want to be part of something special.

• Develop and maintain API services using Databricks and Azure.
• Implement and manage Azure Cache (Redis) and Azure Redis.
• Utilize Databricks Delta Live tables for data processing and analytics.
• Integrate the platform with Snowflake for data storage and retrieval.
• Collaborate with cross-functional teams to deliver the platform in an agile manner.
• Ensure the platform supports offline analytics, ML models, lookup tables, and pricing actions.
• Conduct load, end-to-end, and performance testing.
• Produce pipeline code for running ML Ops jobs and create an Azure DevOps (GitHub) process for source control and deployment.