Policy Expert is seeking an experienced MLOps / Machine Learning Engineer to play a leading role in the design and evolution of our next-generation ML platform on Google Cloud. This is a high-impact individual contributor role for an engineer who enjoys coding, automation, and bringing order to complex DS/ML ecosystems.
What You'll Do
- 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 for training, evaluation, registry, and monitoring.
- Develop MLOps frameworks and SDKs around metadata tracking, feature versioning, model governance, and CI/CD integration using tools like Cloud Build, Terraform, and 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.
- Support platform adoption by mentoring ML engineers and data scientists, and contributing to shared documentation, examples, and tooling.
- Stay close to emerging trends in ML systems, generative AI, and agents; evaluating their fit within the MLOps landscape.
What We're Looking For
- A degree in Computer Science, Software Engineering, Data Science, or another quantitative field.
- 4+ years of experience building and deploying production ML systems.
- Expert-level Python engineering skills: writing clean, testable, modular code suitable for CI/CD environments.
- Proven track record of designing MLOps or ML platform tooling, not just consuming it, such as 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 with Docker, Terraform, and GCP IAM.
- A collaborative, pragmatic mindset: equally comfortable discussing architecture with engineers and practical trade-offs with data scientists.
Nice to Have
- Familiarity with neural network frameworks such as PyTorch or TensorFlow, and interest in GenAI or agentic workflows like LangChain or Vertex AI Agents.
- Knowledge of the insurance industry would be an advantage but not essential.
- Deep, hands-on experience with Vertex AI services and GCP services such as BigQuery, Cloud Storage, and Cloud Run.
Technical Stack
- Python, Vertex AI, BigQuery, Dataflow, Cloud Run, Docker, Terraform, GCP IAM
- GitHub Actions, Cloud Build, Cloud Composer, Pub/Sub
- PyTorch, TensorFlow, LangChain
Benefits & Compensation
- Pension contributions matched up to 7%
- Private medical & Dental cover
- Learning budget of £1,000 a year + Study leave
- Enhanced maternity & paternity
- Travel season ticket loan
- Access to a wide selection of London O2 events and use of a Private Lounge
- Employee Wellbeing Programme
- Prayer room in Office
Work Mode
This is a hybrid role based in London.
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.
