Engineering Fundamentals: Strong knowledge of data structures and design patterns to catch and correct AI errors. Architectural Thinking: Ability to design how complex pieces—APIs, DBs, and third-party tools—fit together. AI Tooling Proficiency: Hands-on experience using tools like Cursor, Claude Code, or GitHub Copilot for real software development. Problem Decomposition: Skill in breaking down vague business requests into concrete, buildable tasks. Technical Skills (Nice-to-Have): Experience with TypeScript, JavaScript, Python, Cloud platforms (AWS/GCP), or ERP systems.
