MLOps Engineer

Hiscox

York and North Yorkshire

On-site

GBP 65,000 - 90,000

Full time

14 days+

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Job summary

Hiscox in the United Kingdom is seeking an experienced Machine Learning Engineer to join the newly formed ML Engineering team. You will develop and maintain infra to deploy data science models, build Python APIs, and ensure scalable, production-grade ML solutions across Azure and AWS.

You’ll collaborate with Data Scientists, Platform Engineers and Developers, work in an Agile environment, implement CI/CD, monitor services, and contribute to model registry and lifecycle management to support

Qualifications

Bachelor's or master's degree in a quantitative field.
3-5 years as an ML engineer.
Experience deploying ML models to production.
Strong Python development skills.
Solid experience with cloud platforms (GCP/AWS/Azure).
Familiarity with Docker and Kubernetes.
Experience with CI/CD and IaC tools.
Agile methodologies experience.

Responsibilities

Develop and maintain ML infrastructure for real-time and batch deployments.
Build Python APIs (Flask/FastAPI) to serve ML models.
Collaborate with data scientists and engineers for scalable deployments.
Design CI/CD pipelines for ML model deployment.
Monitor, test, and maintain ML services in production.
Implement model registry improvements and governance.
Work in Agile teams and participate in iterative development.

Skills

Python development

ML model deployment

Cloud platforms (GCP/AWS/Azure)

Docker

Kubernetes

CI/CD tooling

Agile methodologies

Unit testing

Education

Bachelor's/Master's degree in quantitative field

Tools

Flask/FastAPI

Terraform

Git-based workflows

GitHub Actions

REST APIs

Job description

Company Description

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.

Job Type

Permanent

Build a brilliant future with Hiscox

Company Description

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

The Team

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.

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.

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.
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.
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.