AI Enablement Engineer (Java)Practical experience using AI tools for code analysis, generation, documentation, or testing.Experience in Java and Spring Boot development.Strong knowledge of microservices architecture and API design principles.Experience with RESTful services, JSON, and YAML.Familiarity with CI/CD pipelines (Jenkins, Maven, Git).Understanding of security best practices and performance tuning.Proven ability to operate effectively in regulated, risk-driven enterprise environments.Strong communication skills and the ability to teach and influence without formal authority.Enable effective use of approved AI-assisted development tools such as enterprise copilots, internal GenAI tooling, and approved LLM platforms.Develop and maintain reusable prompt patterns, agent workflows, and repository-level guidance to standardize AI-assisted development across teams.Partner with internal AI governance and architecture groups to pre-align use cases, reduce approval friction, and avoid rework.Apply AI-assisted techniques to analyze legacy codebases, extract business rules, and document undocumented behavior.Support decomposition of monolithic applications into domain-aligned, API-first architectures using AI-driven dependency analysis.Assist teams modernizing legacy stacks including mainframe, batch, and large Java-based systems.Enable AI-assisted generation of unit, integration, contract, and regression tests.Help integrate AI-generated tests into existing CI/CD pipelines without compromising quality, compliance, or auditability.Improve test coverage and reduce manual testing toil across modernization programs.Act as a hands-on coach for engineers and tech leads on compliant, effective AI usage.Create lightweight training materials, examples, and playbooks tailored to real delivery scenarios.Embed with teams temporarily to drive adoption, not just documentation.Ensure AI usage aligns with security, data privacy, PCI, and internal risk requirements.Work closely with architecture, security, and risk partners early in the lifecycle to avoid late-stage blockers.Reduced time spent by teams on legacy analysis, documentation, and test creation.Increased, compliant adoption of AI-assisted development practices.Faster modernization throughput without increased production or compliance risk.Clear evidence of reusable patterns, prompts, and workflows adopted by multiple teams.