AI-Powered SDLC Enablement Lead

Role: AI-Powered SDLC Enablement Lead
*Role Overview*
Hands-on AI development enablement expert to drive the adoption of agentic coding tools — primarily Claude Code and GitHub Copilot — across engineering and product teams. This person will architect and deliver training programs, establish best practices for agentic SDLC workflows, and act as an internal champion for AI-accelerated software development.
*Key Responsibilities*
- Design and deliver structured enablement programs to onboard Development & QA teams onto Claude Code and GitHub Copilot- Implement agentic SDLC workflows — including code generation, review, testing, and documentation pipelines- Define and maintain AI coding standards, guardrails, and governance frameworks for enterprise use- Evaluate and integrate MCP (Model Context Protocol) servers to extend Claude Code capabilities across internal tools- Stay current with the Anthropic and GitHub Copilot ecosystems and surface relevant capabilities to the organization

*Required Qualifications*
AI Coding Platforms
- Hands-on expertise with Claude Code (Anthropic)- Proficiency with GitHub Copilot
Agentic AI & Automation
- Demonstrated experience building or deploying agentic coding workflows — autonomous agents that plan, execute, and validate multi-step development tasks- Familiarity with MCP (Model Context Protocol) — ability to configure, integrate, and extend MCP servers within developer toolchains

Training & Enablement
- Proven track record designing and running internal AI or developer training programs at scale — across multiple teams or disciplines- Ability to translate complex AI concepts into practical, role-specific guidance for developers, QA engineers, architects, and non-technical stakeholders

Enterprise Development Environment
- Solid grounding in enterprise SDLC practices — version control, CI/CD pipelines, code review workflows, and release management- Experience integrating AI tools into IDEs (VS Code, JetBrains)- Awareness of security, compliance, and data privacy considerations when deploying AI coding tools in regulated or enterprise environments