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. Required Qualifications: 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.