Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

DevOps Engineer (MLOps / LLMOps)

Amber Labs
City of London
3 days ago
Create job alert

Senior DevOps Engineer (MLOps / LLMOps)

Clearance: Eligible forBPSS

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.

Related Jobs

View all jobs

DevOps Engineer (MLOps / LLMOps)

DevOps Engineer (MLOps / LLMOps)

DevOps Engineer (MLOps / LLMOps)

DevOps Engineer / Data Engineer - Azure Data Platform

Junior MLOps Engineer

Machine Learning Engineer - Fully Remote

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.