Bullish
Director, AI Enablement
New York, USA · Director
Sponsorship not specified$225k-$325kDetected 91 days ago
Machine LearningLLMsLeadershipCommunication
About the role
- Across Bullish and CoinDesk, we are reshaping how our business teams work, and AI is central to that ambition.
- As Director of AI Enablement, you will define the AI strategy for our business teams and be directly accountable for its execution.
Responsibilities
- Systematically finding where AI creates the most value - proactively mapping opportunities across business units, prioritising ruthlessly, and maintaining a pipeline of high-impact use cases
- Building and shipping AI solutions - from simple copilots to workflow automation to agent-driven orchestration, matching the right approach to the right problem
- Own the AI strategy for business teams across the Bullish Group and be personally accountable for its delivery
- Build, configure, and deploy AI workflows and tools directly, setting the standard for quality and speed
- Drive a structured program that moves fast - prototype, validate, ship what works, kill what doesn't
- Create a self-enablement model that equips business teams to adopt AI tools independently
- Build and lead the AI Enablement function from zero - recruit product managers, engineers, and process specialists as the work demands
- A track record of personally building and shipping solutions - not only managing their delivery
Requirements
- Proficiency with AI workflow automation platforms (n8n, Make, Zapier, or similar) and AI/ML tools (LangChain, OpenAI APIs, Vertex AI, Claude) - able to prototype without waiting for engineering
Skills
- Chief Technology Officer
- Bullish Group is at an inflection point.
- You will own how
- Enabling teams to self-serve - creating the model, tools, and training that let business teams adopt and iterate on AI independently
Compensation
- Bullish US LLC & CoinDesk Inc. are committed to offering competitive compensation and benefits.
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This listing is sourced directly from Bullish's careers page and normalized into a canonical job model.