Lead MLOps Engineer - Telematics

In this role you lead the ML platform and MLOps practices within Hastings' Telematics Data Value team, building scalable infrastructure for the end-to-end ML lifecycle. You will ensure models and data products are reliable, measurable, and deliver business value. Working with cross-functional teams, you’ll advance observability, CI/CD, and reproducibility to support market-leading pricing. This position offers a chance to shape how data-driven underwriting drives sustainable growth and customer trust.

• car allowance
• private medical insurance
• pension contribution match up to 10%
• annual bonus (4Cs performance bonus)
• 27 days annual leave
• flexible hybrid working

• Provide technical leadership on ML platform architecture and operating model
• Develop and maintain automated workflows for feature engineering, model training, testing, deployment, monitoring, and retraining
• Collaborate with Data Scientists, Data Engineers, and ML Engineers to operationalise models and data products
• Establish best practices for ML observability, model performance monitoring, CI/CD, versioning, governance, and reproducibility
• Improve reliability, scalability, and efficiency of ML and data platforms
• Work with cloud-native technologies and infrastructure-as-code for modern ML workloads
• Present technical solutions and outcomes to engineering, operations, and commercial stakeholders
• Evaluate emerging technologies and contribute to engineering standards and best practices

• Strong Python skills and experience with ML solutions in cloud environments
• Experience with MLOps tooling: CI/CD, containerisation, orchestration, model deployment, monitoring, infrastructure automation
• Experience with SQL and large-scale data platforms
• Familiarity with Azure, AWS, or GCP
• Experience collaborating across Data Scientists, Data Engineers, Software Engineers, and stakeholders
• Understanding of software engineering fundamentals: testing, version control, code reviews, observability

• business outcomes orientation
• problem-solving
• independence with collaborative mindset
• Python
• MLOps tooling and practices (CI/CD, containerisation, orchestration, deployment, monitoring, infra automation)
• SQL and large-scale data platforms