Responsibilities
- 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).
- Optimise data processing and orchestration using BigQuery, Cloud Composer and Pub/Sub
- Act as a bridge between Data Science, Product, and Platform teams to ensure smooth delivery of ML solutions
- Review architecture, design decisions, and code to maintain high engineering standards
- Foster a culture of engineering excellence, collaboration, and continuous learning within the team.
- Stay close to emerging trends in ML systems, generative AI, and agents; evaluating their fit within the MLOps landscape.
Requirements
- Degree in Computer Science, Software Engineering, Data Science or another quantitative field
- 6+ years of experience in building and deploying ML systems
- Ability to balance being a hands-on Engineer while also leading or mentoring a team of Engineers
- Strong communicator who can work effectively with Data Scientists, Product Managers and Engineering teams
- Highly proficient in Python: writing clean, testable, modular code suitable for CI/CD environments
- A track record of designing MLOPs or ML platform tooling, not just consuming it.
- Strong understanding of model lifecycle automation, including reproducibility, validation, drift detection and rollback strategies
- Solid grasp of containerisation and infrastructure-as-code (Docker, GCP, IAM)
- A collaborative, pragmatic mindset and very comfortable discussing architecture with Engineers, Data Scientists and non-technical stakeholders
Nice to Have
- Familiar with neural network frameworks such as PyTorch with an interest in GenAI or agentic workflows (LangChain, Vertex AI Agents, etc…)
- Knowledge of the industry would be a plus but not essential
Benefits
- Based in London office in a 50/50 Hybrid mode
- Match pension contributions up to 7%
- Private medical & Dental cover
- 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
- Employee Wellbeing Programme
- Prayer room in Office
Team
Structure: Expanding Data Science function with focus on MLOps platform; team includes Machine Learning Engineers and Data Scientists
Additional Information
- Next steps: Aim to be in touch within 14 working days of application
- Equal opportunity employer committed to diversity and inclusion
- Reasonable adjustments available during interview process
- Encouraged to apply even if not meeting all requirements
