Machine Learning Engineer

Chubb is the world’s largest publicly traded property and casualty insurer. With operations in 54 countries, Chubb provides commercial & personal property and casualty insurance, personal accident and supplemental health insurance, reinsurance, and life insurance to a diverse group of clients. Chubb is also defined by its extensive product and service offerings, broad distribution capabilities, exceptional financial strength and local operations globally.

This role is aimed at designing, developing, and deploying Machine Learning solutions to solve complex business problems. The role will need to work in close partnership with multiple stakeholders including Business team, Data Scientist, IT for the solution deployment. The ideal candidate should have a strong background in software engineering and some level of familiarity with Machine Learning techniques. Additionally, the role will be responsible for defining and designing the infrastructure requirement to support Machine Learning workloads in a cloud-based environment. The person will work closely with the IT team to ensure that the infrastructure is scalable, reliable, and secure. The role will be based in London.

Requirements

• Collaborate with Data Scientists and IT teams to develop and deploy scalable and efficient Machine Learning models.
• Build and implement CI/CD pipelines and workflows for Machine Learning applications.
• Maintain data pipelines to ensure data quality and integrity. Tune data loads.
• Ensure high quality code that meets business objectives, quality standards and secure web development guidelines.
• Build reusable tools to streamline the modeling pipeline and allow for knowledge sharing.
• Build real-time monitoring and alerting systems for Machine Learning systems.
• Develop and write testing queries to ensure high quality models.
• Maintain validation infrastructure.
• Manage project stakeholder expectations and issue communications on progress.
• Design solutions for managing highly complex business rules within the Azure ecosystem.
• Stay abreast of emerging trends in ML Ops and identify opportunities to improve the existing infrastructure.