Job Description – ML Engineering Manager
About Hiscox:
At Hiscox we care about our people. We hire the best people for the work, and we’re committed to diversity and creating a truly inclusive culture, which we believe drives success. We embrace hybrid-working practices, balancing the ability to work remotely with the culture and energy we experience when we are face-to-face in our offices. Our focus on collaboration and cross- functional working is supported with virtual tools that minimise physical travel, hot-desking neighbourhoods that create a physical sense of community and Team Charters that our teams co- create to set out how they’ll work together. This modern way of working has contributed to impressive employee engagement scores across Hiscox and means we’re delivering even better solutions for our Hiscox Colleagues. As an international specialist insurer we are far removed from the world of mass market insurance products. Instead we are selective and focus on our key areas of expertise and strength - all of which is underpinned by a culture that encourages us to challenge convention and always look for a better way of doing things. We insure the unique and the interesting. And we search for the same when it comes to talented people. Hiscox is full of smart, reliable human beings that look out for customers and each other. We believe in doing the right thing, making good and rebuilding when things go wrong. Everyone is encouraged to think creatively, challenge the status quo and look for solutions. Scratch beneath the surface and you will find a business that is solid, but slightly contrary. We like to do things differently and constantly seek to evolve. We might have been around for a long time (our roots go back to 1901), but we are young in many ways, ambitious and going places. Some people might say insurance is dull, but life at Hiscox is anything but. If that sounds good to you, get in touch. You can follow Hiscox on LinkedIn, Glassdoor and Instagram (@HiscoxInsurance)
Position: ML Engineering Manager Reporting to: Head of Data Engineering Location: York or Lisbon Type: Permanent Band: II
The Team This role forms part of the Group and Enterprise Services (GES) team lead by the CTO for GES who are accountable for the full life cycle of around 140 applications. GES has several service verticals, including Business Applications made up of 6 value streams and an Enterprise Application team, Data, End User Experience, Core Engineering, Architecture, and Portfolio Management. The role will sit within the Data service vertical, led by a Head of Data Engineering, and reports into the Head of Data Engineering. Machine Learning Engineer We are looking for an experienced Machine Learning Engineer to lead a newly formed ML Engineering team. As a ML Engineering Manager at Hiscox, you will play a key role in building and maintaining the infrastructure to acquire data from the data platform, deploy models, maintain,
monitor and upgrade core data science services in GCP – Vertex AI (essential) and Azure (desirable) that supports the deployment of machine learning models across the enterprise. You’ll work closely with Data Scientists, Platform Engineers, and Developers to ensure seamless integration and scalable, production grade machine learning solutions. This is a hands-on engineering manager role focused on developing APIs, infrastructure, and deployment pipelines for machine learning models. You’ll be expected to write clean, reusable code, follow best practices in cloud and software engineering, and contribute to the operational excellence of our machine learning systems. In addition to strong engineering skills, you’ll bring a solid understanding of Data Science principles. You should be comfortable reading, questioning, and interpreting machine learning models to ensure they are deployed appropriately and effectively. Your ability to bridge the gap between model development and production deployment will be key to delivering robust, high impact machine learning solutions. You’ll be expected to understand and implement methodologies from the ML OPs life cycle. You’ll also be expected to work in an Agile environment, contributing to iterative development cycles, collaborating across disciplines, and adapting quickly to changing requirements. Key Responsibilities
• Line Management of the ML Engineers, leading the recruitment and onboarding of new engineers when relevant and identifying gaps in capacity and capability.
• Oversee your team’s deployment of ML capabilities and provide support to the Head of Data Engineering, specifically around capacity and delivery of the portfolio.
• As a Team Lead there is an expectation of coaching and mentoring your team members - and supporting the Head of Data Engineering in terms of overall value stream management - especially with partner resources.
• Influence key architectural decisions early on based on requirements of the business, budgets and resiliency. From there working within the values streams to realise this. Moving from a POC to a production ready platform.
• Coach, mentor and influence ML Engineers into greater ML maturity
• Experience building a platform as a service product on top of cloud architecture
• Identifying bottlenecks and using engineering practices to improve the processes
• Taking business requirements and turning it into a solution design diagram and iterating on
it
• Taking a solutions diagram and breaking that down into delivarable pieces of work and
milestones
• Develop and maintain infrastructure for deploying ML models in both real-time and batch
environments.
• Build and maintain Python APIs (Flask/FastAPI) to serve ML models.
• Collaborate with cross discipline engineers to integrate ML services into user-facing
applications.
• Work with platform engineers to align with infrastructure best practices and ensure scalable
deployments.
• Review pull requests and contribute to code quality across the MLE team.
• Monitor and maintain cloud-based ML services, ensuring reliability and performance.
• Design and implement CI/CD pipelines for ML model deployment.
• Write unit tests and follow object-oriented programming principles to ensure maintainable
code.
• Support data modelling and cloud networking tasks as needed.
• Contribute to the development and improvement to our model registry, including tracking
and implementation of model discontinuation upgrades and model monitoring.
• Ownership of the deployment framework for all data science services. You will have
oversight of how data will flow into the data science life cycle from the wider business data
warehouse
• Oversight of the automation of the data science life cycle (dataset build, training, evaluation,
deployment, monitoring) when we move to production
• Interest and ability to work closely with a team and collaborate on all aspects of the data
science and deployment lifecycle
• Work collaboratively with data scientists, data engineers and other technical teams in order
to help support maturation of analytics practice within the organization
• Writing high quality python code using industry best practice for model training and
deployment
Person Specification To succeed in this role, you’ll typically have:
• Bachelor's/Master's degree in a quantitative field (e.g., Computer Science, Statistics,
Mathematics, Physics, Engineering) or equivalent.
• 5+ years as an ML engineer
• Good understanding of core data science principles and understanding of challenges of
migrating research code into production code
• Hands on experience of GCP and machine learning engineering, including deploying,
monitoring, and maintaining ML models in production environments (Neural networks,
Random forests etc.)
• Experience in financial services or insurance with high amounts of regulation is an advantage
but not required.
• Solid experience as a Python developer, ideally in a machine learning engineering context
(Flask/FastAPI, OOP, unit testing)
• Strong understanding of software engineering best practice.
• Experience with TDD.
• Experience with infrastructure as code tools like Terraform or similar Infrastructure as Code
(IaC) tools
• Hands on experience with cloud platforms (GCP, AWS, or Azure).
• Familiarity with containerization using Docker and orchestration of deployments.
• Experience with CI/CD tools and Git-based development workflows.
• Understanding of API operations monitoring and logging.
• Strong problem-solving skills and ability to work independently on technical tasks.
• Familiarity with Agile methodologies and experience working in Agile teams.
• Able to articulate on processes and tools utilised to ensure quality, stability, performance, scalability, deployment, security, maintenance and documentation.
• Creative, proactive, logical and innovative – you do not accept the status quo – and will push hard for innovation and automation.
• Highly results driven, with the energy and determination to succeed in a very fast paced environment where the pace of response is critical to success.
• Ability to work as part of a small team that is part of a larger product division
• Proven communication and presentation skills
• Comfortable in a rapidly changing environment