AI-Native Java Full Stack Engineering Lead, DXC Technology

• 10+ years of software engineering experience, with deep hands-on expertise in Java, Spring Boot/Spring Framework, and full stack development (front-end frameworks such as Angular or React, REST/microservices, relational and NoSQL data stores)
• 3+ years in an engineering leadership role (architect, engineering manager, or technical director) leading teams of 10+ engineers
• Demonstrated hands-on experience integrating AI coding assistants or agentic tools (e.g., GitHub Copilot, Cursor, Claude Code, Amazon Q Developer, or similar) into real development workflows — not just evaluation, but production adoption
• Strong understanding of modern CI/CD, DevSecOps practices, and cloud-native architecture (AWS, Azure, or GCP)
• Experience designing or redesigning engineering processes/playbooks at scale (multiple teams or a large program)
• Excellent communication skills — able to translate technical AI-adoption strategy into business outcomes for both engineering staff and executive stakeholders

Desirable

• We are looking for a hands-on engineering leader to transform how our Java full stack teams build software, moving them from traditional development practices to an AI-native engineering model
• This leader will work with our client to build the strategy, tooling, and cultural shift required to embed AI agents and copilots into every stage of the software development lifecycle, from requirements and design through coding, testing, and deployment
• This is not a "add Copilot licenses" role
• It's a mandate to fundamentally redesign how a Java engineering organization plans, builds, and ships — treating AI agents as first-class collaborators alongside human engineers, with new workflows, new quality gates, and new skills to match
• 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