ML Platform Engineer Jobs

• London
• Hybrid
• Mid-level
• Permanent

Design, build and deploy secure, scalable cloud infrastructure for AI and ML workflows, working in a cross-functional team with data scientists and ML engineers. The role involves infrastructure-as-code with Terraform, containerised deployments on Docker and Kubernetes, CI/CD and GitOps, plus client-facing requirements gathering and technical scoping.

Azure · AWS · Terraform +4

• London

Builds and owns the LLM access layer for agents on the PhysicsX platform, serving self-hosted open models and routing each task to the best-fitting model or combination. Sets technical direction for inference engines like vLLM and SGLang and serving platforms such as KServe, including air-gapped deployments, GPU autoscaling, batching, caching and quantisation. Works with Research and Post-Training specialists to move tuned models into production routes.

vLLM · SGLang · KServe +6

• London
• Hybrid
• Mid-level
• Permanent

Builds and operates cloud infrastructure, automation and deployment systems, primarily on Azure, using Terraform and Ansible for infrastructure as code. Supports CI/CD pipelines, containerised workloads, monitoring, observability and cost optimisation, and handles incident response and root-cause analysis. Works closely with engineering teams to improve the developer experience.

Azure · AWS · GCP +4

• London
• Hybrid
• Permanent

Design, build and operate shared infrastructure for language model access, agent runtimes and observability, taking projects from problem definition through technical design, deployment and ongoing operation. Partner with security, IT and engineering teams, and support adoption through troubleshooting, documentation and reusable patterns.

Kubernetes · MCP · RAG

• London
• Hybrid · 2–3 days in office

Owns the infrastructure behind speech AI models, building scalable data pipelines for ingesting, transforming and validating large training datasets, plus web scraping and data acquisition tooling. Also operates the platform for training, evaluating and serving models, optimising GPU utilisation, scheduling and observability across distributed systems.

Docker · Kubernetes

• Cambridge
• Hybrid · 2–3 days in office

Own the infrastructure behind speech AI models, building scalable data pipelines for ingesting, transforming and validating large training datasets, plus web scraping and data acquisition. Operate the platform that trains, evaluates and serves models in production, optimising GPU utilisation, scheduling and observability across distributed systems.

Docker · Kubernetes

• United Kingdom
• Remote
• Permanent

Builds and operates core platform foundations covering workspaces, user management, API keys, login, analytics and admin tooling, or generic resource and permissioning services used by product teams. Owns features end to end from backend services to settings UI, with safe rollout and migration of live, business-critical systems serving millions of users.

Kubernetes · OAuth · SSO +2

• London
• Director · manages people
• Permanent

Lead the platform engineering team owning hybrid on-prem and cloud compute, Kubernetes clusters, and MLOps infrastructure such as Kubeflow notebooks, GPU training and model serving. Set architectural direction, mature CI/CD, observability and security controls, and ensure scientific compute meets the needs of ML and data science teams. Reports to the VP of Engineering in a matrixed TechBio environment.

Kubernetes · Kubeflow · ISO 27001 +2