ML Ops Engineer (ID: YwDsvzBC)

The ML Ops Engineer role is Remote First, with occasional travel to our London office (approximately one day per month).

You’ll work closely with our data science and data platform teams, taking ownership of deploying AI and machine learning models into production. You’ll help build and operate a platform that is scalable, reliable, and easy to maintain—enabling the business to test ideas safely, launch models quickly, and monitor their real-world performance. Your work will play a key role in shaping the future of our data and ML platform.

As an MLOps Engineer, you’ll collaborate with product managers, data scientists, platform engineers, and data engineers to design and deliver predictive models that improve how we operate. Beyond deployment, you’ll support the data science team by monitoring model performance, setting up alerts, and ensuring models continue to deliver value over time. This role offers exposure to a wide range of projects and the opportunity to see the direct impact of your work across the business.

We value innovation and continuous improvement, so you’ll be encouraged to stay up to date with best practices in MLOps and developments within the pet insurance industry. You’ll also have the opportunity to experiment with and introduce new AI models, technologies, and frameworks to keep our data and modelling capabilities modern and effective.

Responsibilities:

• Design, build, and deploy AI and machine learning systems in production to solve real business problems.
• Translate problem statements into scalable AI/ML solutions, with a focus on performance, reliability, and maintainability.
• Own the end-to-end engineering of AI/ML pipelines, from data ingestion through deployment and monitoring.
• Help shape and evolve our MLOps strategy, including model monitoring, retraining pipelines, versioning, and deployment best practices.
• Evaluate and implement new tools and frameworks to improve the full AI/ML lifecycle, from experimentation to production.
• Collaborate with product managers, engineers, and data engineers to integrate models and ensure robust data pipelines and infrastructure.
• Apply advanced statistical analysis, machine learning, and data mining techniques to identify patterns and generate actionable insights.
• Communicate complex models and insights to stakeholders through visualisations, reports, and presentations.
• Stay informed on emerging trends in data science, ML/AI, and the pet insurance industry, applying new approaches to improve workflows.
• Participate in Agile or Kanban ways of working within a collaborative, flexible team environment.
• Ensure compliance with data privacy, security, and regulatory requirements.

ML Ops Engineer Skills and Experience (Essential)

• Hands-on experience deploying and managing machine learning workflows on Google Cloud Platform, particularly Vertex AI (training, endpoint deployment, and monitoring).
• Experience designing and maintaining CI/CD pipelines for deploying models to production.
• Strong experience with cloud infrastructure and Infrastructure as Code (IaC), ideally using Terraform.
• Solid understanding of data governance, data lineage, and security best practices.
• Ability to communicate effectively with both technical and non-technical stakeholders.
• Comfortable working in an Agile or Kanban environment within a fast-paced scale-up.

Desirable:

• Experience with online and offline feature stores.
• Experience training models using cloud-based GPUs.
• Hands-on experience with continuous model training and retraining pipelines.
• Experience with model monitoring and explainability techniques.
• Experience working in regulated environments.
• Experience deploying, scaling, and optimising ML and AI models.
• Familiarity with libraries such as Scikit-learn, XGBoost, LightGBM, TensorFlow, or PyTorch.
• End-to-end (full-stack) data science experience, from model training to production deployment.
• Experience in the insurance industry.

Ways of Working:

On a typical day, you’ll work from a laptop using a screen, mouse, keyboard, and headset. You’ll collaborate with colleagues via Zoom, Slack, and email, spending around seven hours a day on your computer. You’ll need a distraction-free workspace and a reliable internet connection of at least 25 Mbps. We’ll support your home setup with best-in-class technology, a contribution towards a desk, and vision support.

Inclusion:

We’re committed to providing equal opportunities for everyone and do not discriminate at any stage of the recruitment process or employment. This includes how we source talent, conduct interviews, determine pay, and provide feedback. To learn more, please download our Approach to Inclusion policy.

Position: ML Ops Engineer  -  JobType:  Full-Time  -  Salary: £70,000 to £80,000 per year

Location: This role is Remote First, with occasional travel to our London office (approximately one day per month).