MLOps & Platform Engineer: Build Scalable AI Infra

DGH Recruitment; England; On-site; GBP 90,000 - 120,000; Full time

Job summary

DGH Recruitment is partnering with a leading organisation to recruit an experienced MLOps / Platform Engineer. You will design, deploy, and operate a secure, scalable AI platform and supporting agent infrastructure in collaboration with Data Science and Engineering teams.

You will build robust deployment pipelines, implement observability, and ensure high platform reliability, security, and cost efficiency across AI workloads and deployments.

Qualifications

• 5+ years’ experience in Platform Engineering / DevOps.
• Strong AWS experience.
• Proven knowledge of CI/CD, automation, and DevOps best practices.
• Experience with Kubernetes / containerisation technologies.
• Experience with observability tools (e.g. OpenTelemetry, Datadog).
• Understanding of security, performance optimisation, and scalability.
• Experience working on AI / ML platforms or deployments.

Responsibilities

• Design, deploy, and manage AI platforms and agent infrastructure
• Build and maintain CI/CD pipelines and DevOps workflows
• Implement observability, monitoring, and logging solutions
• Optimise performance, scalability, and cost efficiency
• Support AI teams with infrastructure, deployment, and integration
• Ensure platform security, compliance, and high availability
• Automate infrastructure using Infrastructure as Code (IaC) tools
• Troubleshoot and resolve platform issues

Skills

Platform Engineering

DevOps

AWS

CI/CD

Kubernetes

Observability

Security & Performance

AI/ML platforms

Tools

OpenTelemetry

Datadog

Terraform

Job description

DGH Recruitment is partnering with a leading organisation to recruit an experienced MLOps / Platform Engineer. You will design, deploy, and operate a secure, scalable AI platform and supporting agent infrastructure in collaboration with Data Science and Engineering teams.

You will build robust deployment pipelines, implement observability, and ensure high platform reliability, security, and cost efficiency across AI workloads and deployments.