Demonstrated exposure to, or hands-on experience with, GenAI coding assistants used across day-to-day engineering workflows in the SDLC—implementation, refactoring, unit testing, regression support, code reviews, scripting/automation, troubleshooting, and documentation—while applying engineering judgment and validation. Practical familiarity with tools such as GitHub Copilot or Claude Code is expected.
Uses AI assistance for common dev tasks (code suggestions, test generation, review support, automation scripts, troubleshooting) with clear ownership for correctness and quality.
Applies GenAI in ways that reduce context switching and accelerate delivery across typical workflows (not limited to writing new code).
Demonstrates practical familiarity with GitHub Copilot and/or Claude Code within IDE/CLI-style workflows.
Working knowledge of agentic workflows, spec-driven development (translating low-level design artifacts such as class structures, API contracts, and data models into structured specs that guide AI-assisted implementation), and custom instructions and prompt engineering, with the ability to establish team-level practices for effective AI-assisted development.
- 7+ years of professional software engineering experience in actively developing large scale software using: Java, Spring framework, cloud development, and Web Services supporting high volume transactions in a highly available environment.
- 2+ years of hands-on experience with AI/ML implementation and production deployment
- 6+ years of experience working with containers and microservices in the cloud.
- 6+ years of experience in cloud deployment in a continuous integration, and continuous delivery model (CI/CD)
- 2+ years of experience with Pivotal Cloud Foundry (PCF), or AWS or GCP.
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