Coin Market Cap Ltd - Technical AI Product Manager
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Requirements
• 1.Strong product judgment and the ability to make good calls under ambiguity. • 2. Hands-on Python prototyping ability: you move fast, write clean code, and can translate ideas into working prototypes. • 3. Practical LLM experience + intuition: you understand prompt iteration, context design, and have a strong intuition for how to build useful products on top of LLMsA strong evaluation mindset: you can define quality, test for failure modes, and prevent regressions without heavy process. • 4. High-agency execution: you can go from “vague problem” → “shipped learning” with minimal supervision. • 5. Excellent communication skills (verbal and written): convey complex messages clearly and simply, and driving conviction across stakeholders. • 1.Shipped user-facing AI features (chat, agents, copilots, summarization, search/Q&A, personalization). • 2. 0 to 1 experience in fast-moving environments and owning ambiguous problems end-to-end. • 3. Experience building tool-using and agent-like workflows. • 4. Experience and interest in cryptocurrency. • We have a strong preference for candidates who can point to things they’ve built (prototypes, side projects, or shipped features) and explain how they navigated ambiguity to reach a useful outcome. To stand out, include examples of these in your application.
Responsibilities
• Includes but not limited to: • 1. Identify the biggest user pain points where a crypto AI can materially improve outcomes. • 2. Turn ambiguous ideas into a clear MVP, with crisp scope, constraints, and success metrics. • 3. Prototype full AI experiences in Python to validate value and quality before we ship to production. • 4. Own prompts and context engineering: instruction design, context shaping, guardrails, tool/function calling patterns, and output formatting. • 5. Build practical evaluation loops: golden sets, scenario coverage, qualitative rubrics, regressions, and acceptance criteria. • 6. Design the AI user experience: make it clear, trustworthy, and resilient if things go wrong. • 7. Run fast experiments, learn from real outputs and usage data, and iterate quickly. • 8. Partner with Engineering to ship: provide handoff specs, edge cases, evaluation results, and support debugging and iteration post-launch. • 9. Work on whatever surface is the highest leverage.
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