Machine Learning Engineer

We are looking for an experienced machine learning engineer to join a newly formed ML Engineering team. As a Machine Learning Engineer 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 both Azure and GCP 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 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.

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.

3-5 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 in machine learning engineering, including deploying, monitoring, and maintaining ML models in production environments (Neural networks, Random forests etc.)

Experience in financial services or insurance 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.

Work with amazing people and be part of a unique culture