MLOps Engineer

Job Type: Permanent

Build a brilliant future with Hiscox

Hiscox is a diversified international insurance group with a powerful brand, strong balance sheet and plenty of room to grow. Listed on the London Stock Exchange and headquartered in Bermuda (with the bulk of group leadership sitting in London), Hiscox has over 3,000 staff across 14 countries and 34 offices. Structured by geography and product, Hiscox's long-held business strategy has helped them grow from a niche Lloyd’s underwriter to an international insurance group with a powerful consumer brand.

Hiscox is comprised of the following business lines:
London Market Reinsurance & Insurance Linked Securities (ILS) Retail:
Hiscox USA Hiscox UK Hiscox Europe For the financial year 2022 GWP grew to $4.425m, with net premiums earned growing to $2.928m. Hiscox’s

Purpose:

“We give people and businesses the confidence to realise their ambitions” Hiscox values:
Courage; dare to take a risk Human; clean, fair, and inclusive Ownership; passionate, commercial, and accountable Integrity; do the right thing, however hard Connected; together, build something better

This role forms part of the Enterprise Technology (ET) team lead by the CTO for ET who are accountable for the full life cycle of around 140 applications. ET 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 ML Engineering Manager.

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.

• 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…