Job Type:
Permanent
Build a brilliant future with Hiscox
Position: Machine Learning Engineer
Reporting to: Lead Data Scientist
Location: York
Type: Permanent
Machine Learning Engineer
As a Machine Learning Engineer at Hiscox, you will play a key role in building and maintaining the infrastructure that supports the deployment of machine learning models across the London Market business unit. You’ll work closely with data scientists, platform engineers, and developers to ensure seamless integration and scalable, production-grade machine learning solutions.
You’ll be joining an award-winning team, recognised for its pioneering collaboration with Google to deliver the market’s first AI-enhanced lead underwriting solution, a milestone that reflects our commitment to innovation, impact, and excellence in applying machine learning to real-world insurance challenges.
This is a hands-on engineering 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 lifecycle.
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
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.
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. Hands on experience in machine learning engineering, including deploying, monitoring, and maintaining ML models in production environments. Experience in finance or insurance is an advantage but not required. Solid experience as a Python developer, ideally in a machine learning engineering context. Strong understanding of software engineering best practice. Experience with TDD. Experience with infrastructure as code tools like Terraform. 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.
Key Technical Skills
Python (Flask/FastAPI, OOP, unit testing). Machine learning model experience (Neural networks, Random forests etc.). Terraform or similar Infrastructure as Code (IaC) tools. GCP, AWS, or Azure. Docker and containerised deployments. CI/CD pipelines. Git based development. SQL. Cloud/API operations monitoring. Cloud networking is an advantage but not required.
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