ML Ops Engineer

A day in the life

This role is remote first but travel will occasionally be required to the London office (one day a month).

A day in the life

In this role, you’ll work with both our data science and data platform teams, taking charge of deploying our AI and machine learning models. You’ll help build and run a platform that is scalable, reliable, and easy to maintain – allowing the business to test ideas safely, launch models quickly, and track how they perform in real time. Your work will play a key role in shaping the future of our platform.

As an MLOps engineer, you’ll collaborate with product managers, data scientists, platform services, and data engineers to design and deliver predictive models that improve how we work. After deployment, you’ll continue to support the data science team by monitoring performance and setting up alerts, making sure models keep delivering as expected. This will give you the chance to work on a wide range of projects and see the direct impact of your contributions across the business.

We value innovation and ongoing improvement, so you’ll be encouraged to keep up with the latest practices in MLOps and in the pet insurance industry. You’ll also have the opportunity to test and introduce new AI models, technologies, and frameworks to keep our data and modelling practices up to date and effective.

Your responsibilities

• Design, build, and deploy AI / Machine Learning systems in production to solve business problems.
• Translate problem statements into scalable AI/ML solutions, focusing on model implementation, performance, and reliability.
• Own the end-to-end engineering of AI/ML pipelines, from data ingestion through deployment and monitoring.
• Contribute to shaping and evolving our MLOps strategy, including model monitoring, retraining pipelines, and best practices for versioning and deployment.
• Evaluate and implement new tools and frameworks to improve our end-to-end 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.
• Understanding advanced statistical analysis, machine learning, and data mining to identify patterns and generate actionable insights.
• Communicate complex models and findings to stakeholders through visualisations, reports, and presentations.
• Stay updated on emerging trends in data science, ML/AI, and the pet insurance industry; implement new tools and frameworks to enhance workflows.
• Participate in Agile or Kanban methodologies, contributing to a collaborative, flexible team environment.
• Maintain strong awareness of data privacy and security requirements, ensuring compliance with relevant regulations.

Your skills and experience

Essential:

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

Desirable:

• Experience with online and offline feature stores.
• Experience with cloud-based GPU model training.
• Hands on experience with continuous model training.
• Hands on experience with model monitoring and model explainability.
• Experience working in a highly regulated environment.
• Experience deploying, scaling and optimizing ML and AI models.
• Experience with libraries such as Scikit-learn, XGBoost, LightGBM, TensorFlow, or PyTorch.
• Full-stack Data Science experience from training and deploying AI/ML models.
• Insurance industry experience.