Vice President, AI Strategy & Transformation

Who We Are At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me, Do the Right Thing, and Get Things Done. These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. About the Opportunity We are seeking a VP of AI Strategy & Transformation — a hands-on technical leader who combines deep AI expertise with the strategic vision and executive influence to drive AI adoption across every business unit. This is not a research role or a traditional engineering management position. We need someone who has personally built and shipped production AI systems, understands the frontier of foundation models and agentic AI, and can translate that expertise into company-wide transformation. The ideal candidate is, first and foremost, a deeply technical AI practitioner — someone who has architected and deployed AI systems at scale, and who can channel that credibility into driving AI adoption across every function of the organization. Not a theorist, not a people-manager-only — a builder-leader who ships and who makes others ship. What You’ll Be Doing 1. Company AI Strategy & Transformation Own the company’s AI roadmap end-to-end — from foundation model selection to business-unit-specific deployment plans: - Define and continuously refine the enterprise AI strategy, aligning it to revenue goals, product differentiation, and operational efficiency targets. - Conduct rigorous build-vs-buy-vs-partner analysis for foundation models, AI tooling, inference infrastructure, and data platforms. - Establish an AI governance framework covering model risk, data privacy, bias mitigation, and regulatory compliance across jurisdictions. - Serve as the primary AI advisor to the CEO and executive leadership team; translate frontier AI developments into actionable business implications. - Build and maintain a rolling 6/12/24-month AI transformation roadmap with clear milestones, investment thresholds, and go/no-go decision points. - Identify and evaluate strategic AI acquisition, investment, and partnership opportunities. 2. System Building & Technical Execution Architect and deliver production-grade AI systems that create measurable business impact — not just prototypes: - Lead the architecture of LLM-powered applications including RAG systems, agentic workflows, fine-tuning pipelines, and prompt engineering frameworks at enterprise scale. - Design and implement AI-native infrastructure: model serving, automated evaluation, A/B testing frameworks, version control for prompts and models, and continuous monitoring for quality and drift. - Build and optimize AI agent systems, multi-model orchestration, tool-use chains, and autonomous workflow engines that solve real business problems end-to-end. - Build robust evaluation and benchmarking systems for AI outputs — measuring hallucination rates, task completion accuracy, latency, safety, and end-user satisfaction. - Personally prototype and review critical AI system designs; maintain hands-on technical credibility with the engineering team. 3. Organizational AI Enablement & Adoption Drive AI adoption across every business unit — making AI a core competency for the entire organization, not just the engineering team: - Design and execute a company-wide AI literacy program segmented by role: executive leadership, product managers, engineers, operations, customer-facing teams, and support functions. - Create internal AI tooling, templates, and playbooks that make it radically easy for every business unit to leverage AI capabilities (prompt libraries, no-code/low-code AI interfaces, internal copilots, AI-assisted workflows). - Establish an AI Center of Excellence that serves as the hub for best practices, reusable components, and cross-functional AI project incubation. - Implement a structured AI use-case intake and prioritization process: partner with each business unit to identify high-ROI AI opportunities, scope them properly, and execute with embedded AI support. - Build an AI talent strategy: define hiring profiles for AI engineers and applied AI roles, design technical interview processes, and develop retention programs for top AI talent. - Foster a culture of responsible AI experimentation: psychological safety to try and fail fast, coupled with rigorous post-mortems and knowledge sharing across BUs. What We Look For In You - 10+ years

10+ years in AI, strategy, and transformation; hands-on technical leader who has architected and shipped production AI systems; deep understanding of foundation models and agentic AI; able to translate frontier AI developments into actionable business implications; credible to advise CEO and executive leadership; experience building an AI Center of Excellence and leading enterprise-wide AI adoption.

1. Company AI Strategy & Transformation: Own the company’s AI roadmap end-to-end, define and refine enterprise AI strategy aligned to revenue goals, conduct build-vs-buy-vs-partner analyses for foundation models and AI tooling, establish AI governance across jurisdictions, advise CEO and leadership, maintain a rolling 6/12/24-month AI transformation roadmap, identify strategic AI opportunities. 2. System Building & Technical Execution: Architect and deliver production-grade AI systems at enterprise scale (LLM-powered applications, RAG systems, agentic workflows, fine-tuning pipelines, prompt engineering frameworks), design AI-native infrastructure (model serving, evaluation, A/B testing, version control, monitoring for quality and drift), build AI agent systems and autonomous workflows, develop evaluation/benchmarking for AI outputs, prototype and review critical design with hands-on credibility. 3. Organizational AI Enablement & Adoption: Drive company-wide AI literacy and adoption, create internal tooling and playbooks (prompts libraries, no-code/low-code interfaces, copilots), establish an AI Center of Excellence, implement an AI use-case intake/prioritization process, build an AI talent strategy, foster a culture of responsible AI experimentation with post-mortems and knowledge sharing.