1. AI Transformation Execution

• Embed with business teams (e.g. Compliance, Legal, CS) to understand existing workflows in detail and identify concrete gaps and pain points.
• Translate identified gaps into practical AI-driven solution ideas — not abstract recommendations, but scoped proposals with a clear path to delivery.
• Work directly alongside business teams to implement solutions (AI products, copilots, automated workflows, internal tools) and drive adoption post-launch.
• Track and report on impact — efficiency gains, time saved, quality improvements — to justify continued investment and prioritize the next use case.
• Build reusable playbooks and templates from each transformation project so lessons scale across other business units.

2. System Building & Technical Execution

• For larger, cross-functional initiatives: act as an AI PM — deeply understand business requirements, map them against existing company tech infrastructure, and partner with the relevant engineering/R&D teams to scope, prioritize, and drive delivery.
• For smaller, high-velocity needs: personally build quick-win solutions (vibe-coding internal tools, lightweight automations, prompt-based workflows) and hand them directly to business teams for immediate use.
• Maintain working knowledge of the company's AI tooling and infrastructure (internal copilots, MCP servers, knowledge base systems, agent frameworks) to know what can be reused vs. what needs to be built new.
• Continuously evaluate emerging AI tools and techniques for applicability to internal use cases, and pilot them quickly and cheaply before recommending broader investment.

3. Organizational AI Enablement & Adoption

• Help run AI literacy and enablement activities for non-technical business units, translating capability into concrete, adoptable use cases.
• Contribute to and maintain internal AI playbooks, prompt libraries, and tooling documentation.
• Act as a first point of contact for business units with an AI use-case idea — helping them scope it, prioritize it, and find the fastest path to a working solution.
• Share learnings and reusable components back into the broader AI enablement function to avoid duplicated effort across teams.

• 4+ years of experience in a role combining project execution, product management, or technical delivery, with meaningful hands-on exposure to AI/LLM-based tools or products.
• Demonstrated ability to independently build and ship practical AI-powered solutions (e.g. internal copilots, automation scripts, prompt-driven tools) — comfortable "vibe coding" a working prototype without waiting on a full engineering team.
• Strong stakeholder management skills — able to sit with a non-technical business team, extract real requirements, and translate them into a scoped, deliverable solution.
• Working understanding of the modern AI stack: LLMs, RAG, prompt engineering, and agentic/automation frameworks — enough to have informed conversations with engineering, not necessarily to architect systems from scratch.
• Comfortable operating with ambiguity and switching between "hands-on builder" and "AI PM working with R&D" depending on the size and complexity of the initiative.
• Bachelor's degree in a related field (Computer Science, Engineering, Business, or equivalent practical experience).

• Experience in fintech, crypto/blockchain, or another regulated industry.
• Prior experience working cross-functionally with Compliance, Legal, GBS, or similar non-P&E functions.
• Familiarity with internal enterprise tooling ecosystems (Lark/Feishu, MCP-based integrations, internal data platforms).
• Experience running or contributing to an internal AI Center of Excellence, enablement program, or similar cross-team initiative.