Hands-on familiarity with leading Large Language Models (LLMs) (e.g., Anthropic Claude (Opus, Sonnet, Haiku), OpenAI GPT-4/5 and o-series, Google Gemini, Meta Llama, and Mistral), with a practical understanding of model selection trade-offs (reasoning depth, context window, cost, latency, data residency). Demonstrated interest or working proficiency in "vibe coding" and AI-assisted development workflows using tools (e.g., Claude Code, Cursor and GitHub Copilot), sufficient to prototype control automations, evidence collectors, and governance tooling without dependence on engineering backlog.