DevOps Engineer (MLOps / LLMOps)

Senior DevOps Engineer (MLOps / LLMOps)

Clearance: Eligible for BPSS

Start: ASAP

Work pattern: Hybrid (London)

Work type: 12 month FTC (Competitive Salary)

We're working with a major UK government initiative that's shaping the future of how citizens interact with public services. The programme focuses on harnessing the latest AI technologies to deliver simpler, faster, and more efficient digital experiences across departments.

We're looking for an experienced Cloud & DevOps Engineer to join the AI Customer Experience team - a multidisciplinary group driving innovation in Generative AI, conversational interfaces, and automation. You'll design, build, and manage the infrastructure that powers large-scale AI services and agentic workflows across government systems.

Key Responsibilities

• Design and provision secure, scalable cloud infrastructure (AWS, Azure, or GCP) using Python-based Infrastructure as Code (Terraform or Pulumi).
• Build and optimise CI/CD pipelines to automate the deployment of AI applications and models (MLOps / LLMOps).
• Containerise workloads using Docker and manage deployments through Kubernetes (KubeRay, Kubeflow, MLRun, or similar).
• Provision and manage the infrastructure required to run AI workloads, including vector databases and hyperscaler AI services.
• Develop automation scripts in Python to streamline operations and reduce manual tasks.
• Implement comprehensive monitoring, logging, and alerting to maintain high system reliability and performance.
• Provide technical support for complex issues and advise on modern engineering practices for large-scale projects.

Skills & Experience

• Strong background as a DevOps or Cloud Engineer in public cloud environments (AWS, Azure, or GCP).
• Experience deploying and managing infrastructure for AI/ML workloads using MLOps or LLMOps practices.
• Excellent scripting and automation skills in Python (e.g. Boto3, SDKs).
• Proven experience with Python-based IaC frameworks (Pulumi, Terraform, CDKs).
• Hands-on experience building CI/CD pipelines for AI deployments (Github Actions, MLFlow, ZenML, or similar).
• Deep understanding of containerisation and orchestration tools (Docker, Kubernetes).

Desirable

• Experience deploying AI inference engines (vLLM, Ray Serve, Triton).
• Familiarity with observability tools for LLMs (TruLens, Helicone, LangSmith).
• Understanding of AI safety and reliability frameworks (Guardrails AI).

This is an exciting opportunity to help define the infrastructure powering the next generation of AI-driven public services. If you have the experience and passion to work on impactful projects within government, we'd love to hear from you.