Practical AI fluency: experience deploying AI/ML workflows into product development processes, building or shipping products with AI capabilities, and a working understanding of core concepts (LLMs, agents, RAG, prompt engineering) sufficient to evaluate tools, guide implementation, and hold technical teams accountable. Hands-on proficiency with the modern product stack: analytics platforms (Amplitude, Mixpanel), collaboration and roadmapping tools (Linear, Jira, Productboard), experimentation frameworks, and data warehousing — plus working familiarity with AI and automation platforms (n8n, Zapier, Make, or comparable tooling).