AI/ML Infrastructure Engineer
£133,000–£231,000/year (£11,083–£19,250/month) — Negotiable
Job Description
Build the platform layer behind training, inference, GPU scheduling, and model-serving capacity. The work covers Kubernetes operations, observability, deployment pipelines, and practical capacity planning for AI workloads.
Requirements
- Experience running Kubernetes, containers, and cloud infrastructure for production systems
- Practical knowledge of GPU orchestration, model-serving platforms, or ML workflow tools
- Strong debugging skills across networking, compute scheduling, storage, and CI/CD
- Comfort working with ML engineers on training pipelines and inference reliability
- Practical knowledge of GPU orchestration, model-serving platforms, or ML workflow tools
- Strong debugging skills across networking, compute scheduling, storage, and CI/CD
- Comfort working with ML engineers on training pipelines and inference reliability
Responsibilities
- Operate GPU-backed training and inference environments
- Improve model-serving capacity, deployment safety, and observability
- Build CI/CD workflows for ML services and infrastructure changes
- Partner with engineering teams on incidents, performance issues, and capacity forecasts
- Improve model-serving capacity, deployment safety, and observability
- Build CI/CD workflows for ML services and infrastructure changes
- Partner with engineering teams on incidents, performance issues, and capacity forecasts
Benefits
- Work on infrastructure that directly supports AI API and hosting customers
- Own practical reliability improvements across GPU and serving systems
- Collaborate closely with ML, backend, DevOps, and support teams
- Own practical reliability improvements across GPU and serving systems
- Collaborate closely with ML, backend, DevOps, and support teams
Job Overview
Employment Type
Full Time
Experience Level
Senior
Location
London, England, United Kingdom
Vacancies
2
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