Machine Learning Engineer

A leading insurer is investing heavily in data science, machine learning, and generative AI to transform how it understands risk, detects fraud, and delivers smarter decision-making across the business.

They are now looking for a Machine Learning Engineer to join a growing data science function and help bring advanced models from research into robust, scalable production environments.

This is a great opportunity to work on greenfield initiatives across fraud detection, intelligent automation, and generative AI use cases, while helping shape the organisation’s ML engineering capability and platform as it continues to evolve.

You’ll work closely with data scientists, data engineers, and software engineers, ensuring machine learning solutions are production-ready, maintainable, and able to deliver real business value.

• Designing and building production-ready machine learning systems
• Automating the end-to-end ML lifecycle, from experimentation through to deployment and monitoring
• Contributing to the development of a scalable ML platform and engineering standards
• Collaborating with data scientists to operationalise models
• Working with engineers and business stakeholders to deliver impactful AI and ML solutions
• Writing high-quality Python code following strong engineering principles
• Supporting architecture discussions and selecting the right modelling and deployment approaches
• Building solutions across both traditional machine learning and generative AI use cases

• Proven experience in Machine Learning Engineering or Data Science within a commercial environment
• Strong Python development skills and good software engineering practices
• Experience deploying machine learning models into production
• Understanding of core machine learning principles and modelling approaches

• Cloud platforms (Azure experience beneficial)
• Databricks or similar data platforms
• Containerisation and orchestration (Docker, Kubernetes or equivalent)
• CI/CD pipelines and modern development tooling
• Version control (Git) and collaborative development practices

• Experience within financial services, insurance, or other regulated environments
• Exposure to LLMs, generative AI or agentic AI solutions
• Experience supporting the ML lifecycle from experimentation to production