Strong understanding of the full PDLC (ideation to operations) with clear insight into where AI can augment each stage.
Hands-on experience with modern AI tooling (GenAI assistants, automation frameworks, dev copilots like GHCP, Claude Code, etc.) and ability to evaluate tools pragmatically.
Solid grounding in Responsible AI: data privacy, security, model risk, ethics, and embedded governance in delivery workflows.
Ability to define and track AI impact metrics (cycle time, defect rates, test coverage, operational stability).
Roles & Responsibilities
Work directly with engineering, product, and QA teams to embed AI into real delivery workflows.
Define AI standards, patterns, prompt guidelines, reference architectures, and reusable assets.
Build and deliver enablement programs (playbooks, workshops, training, coaching).
Integrate AI into toolchains (IDE, CI/CD, testing, monitoring) and rapidly prototype use cases.
Drive adoption through strong communication, executive influence, and measurable outcomes.
Establish a phased AI adoption roadmap aligned to business value.
Ensure AI improvements are cross-functional across engineering, DevOps, security, and compliance.
Redesign workflows to maximize AI value (not just automate existing processes).
Experience & Impact
10+ years in software engineering, platform engineering, or tech consulting with AI/DevOps transformation exposure.
Experience in large, complex, regulated enterprise environments with governance and compliance constraints.
Proven change agent influencing without authority across senior leadership and delivery teams.
Experience building scalable assets (playbooks, templates, frameworks, reference models).
Demonstrated AI impact: reduced cycle time, improved quality, increased automation/test coverage, reduced operational toil.
Proven ability to convert AI experimentation into repeatable, sustainable engineering practices.
Track record of embedding responsible AI practices without slowing delivery, ensuring governance is built-in, not bolted on.