MLOps / Machine Learning Engineer

Are you ready to transform the insurance industry? Policy Expert is a forward-thinking business that loves to get things done. Leveraging proprietary technology and smart data, we offer reliable products and a wow customer experience.

Having achieved rapid growth since being founded in 2011, we’ve won over 1.5 million customers in Home, Motor and Pet insurance and have been ranked the UK’s No.1-rated home insurer by Review Centre since 2013.

We are seeking a MLOps / Machine Learning Engineer to play a leading role in the design and evolution of Policy Expert’s next-generation ML platform on Google Cloud. You’ll work as a hands-on technical expert in building reusable, scalable, and observable ML infrastructure that empowers data scientists and product teams to deliver measurable business impact. This is a high-impact individual contributor role for an engineer who enjoys coding, automation, and bringing order to complex DS/ML ecosystems.

• Design, implement and standardise end-to-end machine learning pipelines using Vertex AI Pipelines, Model Registry, and Cloud Run, with a strong focus on reliability, automation, and cost efficiency.
• Build reusable components and templates to accelerate model delivery across squads (training, evaluation, registry, monitoring).
• Develop MLOps frameworks and SDKs around metadata tracking, feature versioning, model governance, and CI/CD integration (e.g. Cloud Build, Terraform, GitHub Actions).
• Partner with data scientists and pricing analysts to translate model prototypes into fully automated, monitored deployments.
• Optimise data processing and orchestration using BigQuery, Dataflow, and cloud-native patterns (Container, Cloud Composer, Pub/Sub).
• Support platform adoption by mentoring ML engineers and data scientists, and contributing to shared documentation, examples, and tooling.
• Mentor and upskill peers in engineering excellence, code quality, and platform use.
• Stay close to emerging trends in ML systems, generative AI, and agents; evaluating their fit within the MLOps landscape.

• A degree in Computer Science, Software Engineering, Data Science, or another quantitative field.
• 4+ years of experience building and deploying production ML systems.
• Proven track record of designing MLOps or ML platform tooling, not just consuming it (e.g. custom pipeline components, SDKs, or frameworks).
• Strong understanding of model lifecycle automation, including reproducibility, validation, drift detection, and rollback strategies.
• Solid grasp of containerisation and infrastructure-as-code (Docker, Terraform, GCP IAM).
• A collaborative, pragmatic mindset: equally comfortable discussing architecture with engineers and practical trade-offs with data scientists.
• Familiarity with neural network frameworks such as PyTorch or TensorFlow, and interest in GenAI or agentic workflows (LangChain, Vertex AI Agents, etc.) is a plus.
• Knowledge of the insurance industry would be an advantage but not essential.

• Deep, hands-on experience with Vertex AI (Pipelines, Model Registry, Experiments, Model Monitoring) and GCP services such as BigQuery, Cloud Storage, and Cloud Run.

• This role will be based in our London office in a 50/50 Hybrid mode.
• We match your pension contributions up to 7%.
• Learning budget of £1,000 a year + Study leave (with encouragement to use it).
• Enhanced maternity & paternity.
• Travel season ticket loan.
• Access to a wide selection of London O2 events and use of a Private Lounge.

We pride ourselves on being an equal opportunity employer. We treat all applications equally and recruit based solely on an individual’s skills, knowledge, and experience. The quality and growing diversity of our team is a testament to this commitment. At Policy Expert, we are committed to fostering an inclusive and supportive environment for all candidates. If you require any reasonable adjustments during the interview process to accommodate your needs, please do not hesitate to let us know. We are dedicated to ensuring every candidate has an equal opportunity to succeed and will work with you to provide the necessary support. We aim to be in touch within 14 working days of your application – you will be notified if successful or unsuccessful. Please be encouraged to apply even if you do not meet all the requirements.