AI DevOps/Process Change Engineer

AI DevOps/Process Change Engineer

• Hybrid- Remote/London
• 6 months +
• Inside IR35
• SC Clearance (or eligible)

Our customer, a central government organization, are building an enterprise-grade AI platform to enable secure, scalable and production-ready use of Generative AI and ML across the programme.

The successful candidate will have experience in:

• Strong DevOps/SDLC background - CI/CD pipeline design, branching strategy, release management, 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 (eg how process friction - slow CI, manual gates, 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

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.

Key Responsibilities:

• Embed with individual CDS delivery teams to assess current DevOps practice - CI/CD, branching, release cadence, testing gates, review process
• Identify process debt that limits the value of AI coding/delivery tools (eg slow feedback loops, manual approval bottlenecks, 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, 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 (eg cycle time, review turnaround) to evidence the debt-paydown case