Senior MLOps Engineer — Government Digital Services

Priya Mehra

Full-time · Senior · London

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About the role

This role sits within the data and AI directorate of a UK central government department. Our team is responsible for the infrastructure that runs machine learning models used across seven public-facing services — everything from benefits eligibility estimation to fraud signal detection in grant applications. We are a relatively small team for the scope: eleven engineers, four data scientists, and two analysts. We operate within GDS and CDDO standards. We take those standards seriously not because they are mandated but because they exist for good reasons and the services we run affect real people\'s lives. What we are looking for is a Senior MLOps Engineer who understands that working in the public sector means accepting constraints that don\'t exist in commercial environments — procurement cycles, security accreditation, and the need to explain every architectural decision to a non-technical senior civil servant — and who sees those constraints as engineering problems rather than inconveniences. We use AWS GovCloud, Kubernetes, and Airflow. We deploy Python models. We do not use the newest and shiniest tool by default; we use the tool that has the best chance of being maintained, supported, and understood by whoever comes after us. If that approach to platform engineering resonates with you, we would like to hear from you.

Responsibilities

Own and evolve the ML serving infrastructure for seven production models across public-facing government services
Build and maintain Airflow-based retraining pipelines with full audit trails suitable for internal governance review
Define and document MLOps standards for the team: deployment process, model versioning, monitoring, and incident response
Work with security and accreditation teams to ensure infrastructure changes meet GovCloud compliance requirements
Mentor two mid-level engineers and contribute to cross-directorate working groups on AI infrastructure standards

Requirements

6+ years of platform, MLOps, or data engineering with at least 2 years in a regulated or public sector environment
Kubernetes for ML workload orchestration — cluster management, RBAC, and namespace governance in security-conscious environments
AWS at production depth: EKS, S3, IAM, and VPC networking within GovCloud or equivalent constrained cloud environments
Airflow for orchestrating multi-step ML pipelines with dependency management, retry logic, and alerting
Python for infrastructure tooling, automation, and pipeline code — clean, documented, and version-controlled
CI/CD pipeline ownership using GitHub Actions or equivalent — you treat pipelines as software, including tests
Experience navigating security accreditation processes, change management, or similar governance requirements

Benefits

Civil Service pension — defined benefit scheme with 27.1% employer contribution
$92,000 – $112,000 per annum, Senior Civil Service Pay Band equivalent
Hybrid — two days in our London office, three days remote
30 days annual leave plus bank holidays
Work on infrastructure that directly affects the delivery of public services to millions of people