AI DevOps & Engineering Process Lead - CI/CD, GitLab & AI Delivery

We are building an enterprise-grade AI platform to enable secure, scalable and production-ready use of Generative AI and ML across the Customs Declaration Service.

Location: Anywhere in the UK, with the option to attend the office when required.

Mandatory

• Strong DevOps/SDLC background, CI/CD pipeline design, branching strategy, release management and environment management
• Experience assessing and improving existing engineering processes within a delivery team, not just building new pipelines from scratch
• Stakeholder engagement, able to work embedded with delivery teams to understand current-state process, build trust and land change
• Understanding of what "AI-ready" DevOps looks like, e.g. how process friction, slow CI, manual gates and poor branch hygiene, blocks AI coding assistants and agentic workflows from adding value
• Familiar with common CDS delivery toolchain, GitLab, JIRA, Confluence or equivalent enterprise DevOps tooling

Nice to Have

• Experience with AI-assisted development tooling, Copilot-style code assistants, agentic PR workflows and what they need from surrounding process to work well
• Change management / process consulting background
• Exposure to regulated/government delivery environments

Key Responsibilities

The DevOps/Process Change role works directly with CDS delivery teams to understand their existing DevOps processes, identify process debt, and address it so those teams can get genuine value from AI-assisted delivery, not applying AI on top of broken process and expecting it to compensate.

• Embed with individual CDS delivery teams to assess current DevOps practice, CI/CD, branching, release cadence, testing gates and review process
• Identify process debt that limits the value of AI coding/delivery tools, e.g. slow feedback loops, manual approval bottlenecks and inconsistent environment management
• Work with teams to remediate process debt in a practical, incremental way, not a big-bang process overhaul
• Advise teams on how to structure process so AI-assisted development, code assistants and agentic workflows, can be adopted safely and effectively
• Feed findings back to the platform team on recurring cross-team process patterns worth solving once, platform-wide, rather than team-by-team
• Track and report on process improvement outcomes per team, e.g. cycle time and review turnaround, to evidence the debt-paydown case