• Define the AI-native engineering vision for Java full stack delivery (Spring Boot/Spring Cloud, Angular/React, microservices, cloud-native architectures), including target-state workflows, tooling stack, and adoption roadmap.
• Lead tool selection and integration across AI coding assistants, agentic dev environments, automated code review, and AI-assisted testing/QA pipelines — ensuring interoperability with existing CI/CD, IDEs, and version control.
• Redesign SDLC processes to incorporate AI agents at each phase: requirements decomposition, code generation, unit/integration test generation, code review, refactoring, documentation, and incident triage.
• Build and lead a center of excellence (or working group) for AI-native Java development, including reusable prompt libraries, agent configurations, coding standards for AI-assisted output, and governance for AI-generated code quality and security.
• Upskill the engineering workforce, developing training curricula and hands-on enablement programs to shift developers from traditional coding to AI-orchestrated development (prompt engineering, agent supervision, code review of AI output).
• Establish quality, security, and governance guardrails for AI-generated code — including IP/licensing risk, secure coding practices, hallucination detection, and human-in-the-loop review gates.
• Define and track success metrics: developer velocity/throughput, code quality (defect rates, test coverage), cycle time reduction, and cost-to-serve improvements attributable to AI-native practices.
• Partner cross-functionally with delivery leads, account teams, security/compliance, and client stakeholders to pilot AI-native delivery on live engagements and scale what works.
• Stay current on the evolving landscape of AI coding agents, frameworks, and best practices, and continuously evolve the playbook.