ML Infrastructure Lead

Role Overview

• Lead the design and evolution of our ML platform, infrastructure and MLOps capability
• Build and maintain scalable, reliable and secure systems for model training, testing, deployment, monitoring and lifecycle management
• Develop the infrastructure and tooling that enable ML Engineers, Data Scientists and Researchers to work efficiently and ship models with confidence
• Design robust workflows for CI/CD, model versioning, reproducibility, experimentation, feature management and release management
• Own and improve the production environment for machine learning systems, ensuring strong standards for availability, performance, observability and resilience
• Define and implement monitoring across model and platform layers, including system health, data quality, drift, latency, throughput and cost efficiency
• Build or optimise internal self-service tooling and platform capabilities to reduce friction for teams working on ML use cases
• Partner closely with ML, Data, Software and Platform Engineering teams to productionise models and improve the end-to-end ML development lifecycle
• Support the scaling of infrastructure for both training and inference workloads, including high-throughput, real-time or compute-intensive use cases where relevant
• Drive best practice in governance, security, compliance, auditability and operational rigour across the ML lifecycle
• Improve the efficiency and cost-effectiveness of ML systems, including cloud resource usage, compute environments and deployment patterns
• Mentor engineers and act as a technical leader across ML platform and operations topics
• Help define the roadmap for ML enablement, ensuring the platform can support current needs while scaling for future growth

Requirements

• Proven experience in a senior MLOps, ML Platform, ML Infrastructure, Platform Engineering or Machine Learning Systems role
• Strong hands-on background in software engineering and cloud infrastructure, ideally with direct experience supporting production machine learning environments
• Experience building and operating systems that support the full ML lifecycle, from experimentation and training through to deployment and monitoring
• Strong knowledge of Python and sound engineering principles, including testing, automation and code quality
• Strong experience with cloud platforms such as GCP
• Experience with Docker, Kubernetes and modern containerised deployment patterns
• Strong experience with CI/CD pipelines, infrastructure-as-code and workflow orchestration
• Experience with tools such as Airflow or similar platform and orchestration technologies
• Good understanding of model observability, data quality, feature pipelines, lineage and reproducibility
• Experience designing scalable infrastructure for ML workloads, including training, batch inference and real-time serving
• Strong appreciation of reliability, security, governance and operational excellence in customer-facing or production-critical systems
• Ability to operate across both strategic and hands-on technical work
• Strong communication skills and the ability to work effectively across engineering, product and data teams
• Nice-to-haves: Experience supporting computer vision, deep learning, LLM or other compute-intensive ML workloads
• Familiarity with feature stores, model registries and automated retraining pipelines
• Experience building internal developer platforms or self-service ML tooling
• Experience in regulated, high-security or high-availability environments
• Experience leading or mentoring engineers in a scale-up or high-growth technology business
• Familiarity with responsible AI, model governance or risk controls in production ML setting

Tech Stack

• Airflow
• Cloud
• Docker
• Google Cloud Platform
• Kubernetes
• Python

Benefits

• 25 days Annual Leave, plus 8 Bank Holidays (more holiday with service
• up to an extra 5 days off per year based on your continuous service)
• Growth Shares allocated after passing probation (6 months of service)
• Salary sacrifice schemes including: Pension, Cycle To Work and Electric Car Scheme
• Nursery Sacrifice Scheme
• Work Overseas Perk
• Work globally for up to 2 weeks
• Life Assurance
• SmartHealth
• Access to private GP, Psychologist, Nutritionist along with tailored fitness plans for both you and your family
• Benefit from personalized 1:1 career coaching with our in-house Occupational Psychologist
• Award winning L&D platform with personal allocated training budgets
• Enhanced paid family leave
• Pension
• 5% employee, 3% employer
• Flexible hybrid working environment
• Free Barista Coffee/Tea, biscuits with fruit in the WeWork office
• Free access to WeWork discounts and free online well-being sessions
• Vitality Health
• a range of options available on this below. The Vitality Programme includes a number of reward benefits that all employees have access to as part of the plan, for example: Private Health cover including Dental, Optical, and Audiology
• 50% off monthly gym memberships
• Apple watches significantly discounted based member vitality status
• Half price trainers with Runners Need
• Weekly rewards – Free coffee with Café Nero
• Monthly rewards – Free Cinema ticket
• Discounts on travel with Expedia (hotels) and Mr & Mrs Smith with discounts getting greater throughout the year based on a members vitality status
• Amazon prime free months based on activity
• Up to 25% cashback at Waitrose when buying healthy foods
• 75% off stays at Champneys Health Spas
• Allen Carr’s £299 no smoking programme for free
• Access to Vitality Healthy Mind with 30% off Headspace subscriptions and the ability to earn Vitality points for using Buddhify, Calm and Headspace
• Discounts on Weight Watchers
• 50%-80% off Comprehensive Private Health screenings