Description

• Define and operate AI governance controls — approved-tool enforcement, prompt and template standards, code attestation and human approval audit trails.
• Own the AI adoption roadmap across the engagement; drive consistent usage across engineering, quality and analysis roles.
• Establish pre-AI productivity baselines and report adoption and value-realisation metrics to client leadership.
• Define guardrails and review gates for AI-generated code covering security, licensing and compliance.
• Own the project context layer — curation of standards, architecture artefacts and design decisions that ground AI-assisted engineering.
• Act as the interface to the client’s AI enablement function; identify platform gaps and drive joint closure.

Experience & Expertise

• 12–16 years in software engineering or delivery, with recent hands-on ownership of AI-assisted development practices.
• Practical experience with agentic coding tools (Claude Code, GitHub Copilot, Cursor or similar) in a governed production setting.
• Working knowledge of spec-driven development, context engineering, RAG and MCP concepts.
• Experience defining engineering governance, quality gates or compliance frameworks.
• Strong analytical and reporting skills; able to quantify and present productivity impact to executives.
• Awareness of data privacy and regulated-industry constraints on AI usage

Key Skills

Education

Any Gradute