Lead Machine Learning Engineer
Role details
Job location
Tech stack
Job description
This is an exciting moment in our journey as we scale our AI capabilities to support ambitious growth across the business. We're rapidly expanding our Data Science function and investing in the platforms that enable our teams to move fast and deliver impact. At the heart of this is our MLOps platform which is designed to empower Data Scientists to seamlessly experiment, iterate, deploy and monitor models in production. With access to modern, cutting-edge technologies, our goal is to build a best-in-class MLOps platform that accelerates innovations and turns great ideas into real-world solutions that have a genuine business impact.
Your day to day:
As our Lead Machine Learning Engineer, you'll be at the centre of building the ML platform that powers our next wave of AI driven products. You'll design and ship scalable, reusable infrastructure in GCP that enables Data Scientists to experiment quickly and get models into production with confidence. You'll say hands-on while leading and mentoring a team of Machine learning Engineers, helping them grow technically and professionally. Working closely with Data Science and product teams, you'll bridge the gap between experimentation and reliable production systems. This role is perfect for someone loves building things, moving quickly, and combining deep engineering work with growing and leading a team:
- 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
Do you have experience in Terraform?, * A degree in Computer Science, Software Engineering, Data Science or another quantitative field
- 6+ years of experience in building and deploying ML systems
- Able 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
- Familiar with neural network frameworks such as PyTorch with an interest in GenAI or agentic workflows (LangChain, Vertex AI Agents, etc…) is a plus
- Knowledge of the industry would be a plus but not essential
Benefits & conditions
This role will be based in our London office in a 50/50 Hybrid mode.
We match your 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
