Role Summary:

Lead the adoption and scaling of AI-assisted software development across engineering teams using Claude Code, GitHub Copilot, MCPs, Skills, and Agentic AI workflows. Act as a hands-on technical coach, architect, and enablement leader driving developer productivity, software quality, and AI-first engineering practices.

Key Responsibilities:

- Drive adoption of Claude Code and GitHub Copilot across development and QA teams.

- Design and implement AI-powered SDLC workflows for requirements, coding, testing, reviews, documentation, and modernization.

- Build and govern Skills, MCPs, Claude.md configurations, Hooks, Rules, and Specifications.

- Define engineering standards, guardrails, and governance for AI-assisted development.

- Create reusable developer playbooks, onboarding guides, and best practices.

- Deliver training and coaching programs for engineering teams.

- Design and implement multi-agent and sub-agent workflows for complex engineering tasks.

- Integrate Claude Code with tools such as GitHub, Jira, Confluence, Playwright, and internal engineering platforms.

- Measure and improve developer productivity through AI adoption.

Required Experience:

- 8+ years in Software Engineering, Solution Architecture, Developer Experience, Engineering Enablement, or Technical Leadership.

- Hands-on experience with Claude Code and GitHub Copilot.

- Practical experience building or deploying Agentic AI workflows.

- Strong experience implementing and governing MCP (Model Context Protocol) integrations.

- Experience driving AI adoption across multiple engineering teams.

- Strong understanding of Git workflows, CI/CD, Code reviews, and modern SDLC practices.

- Experience with VS Code and/or JetBrains AI-assisted development environments.

- Strong coaching, mentoring, and stakeholder management skills.

Tech Stack & Skills:

- Claude Code: CLI and IDE workflows, Claude.md architecture, Rules, Skills, Specifications, Hooks, Context management, Prompt engineering.

- Agentic AI: Agentic SDLC workflows, Multi-agent patterns, Human-in-the-loop workflows.

- MCP: Design and integration, Toolchain integrations (GitHub, Jira, Confluence, Playwright).

- Engineering Enablement: Team onboarding, AI adoption strategy, Productivity measurement.

Nice-to-Have:

- Knowledge Graphs and RAG for large codebases.

- AI-driven modernization (.NET, Java, Angular).

- AI evaluation and observability platforms.

- GitHub Copilot Enterprise administration.

Preferred Certifications:

- Claude Certified Architect Foundations (CCA-F), GitHub Copilot Certification, AWS/Azure/GCP Certifications, TOGAF.