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 of experience in AI strategy and transformation, with hands-on experience architecting and deploying production-grade AI systems, and the credibility to influence and advise executives. Deep technical AI practitioner with ability to translate frontier AI developments into actionable business impact.

1. Own the company’s AI roadmap end-to-end — from foundation model selection to business-unit-specific deployment plans, including defining and refining enterprise AI strategy aligned to revenue goals and operational targets, performing build-vs-buy-vs-partner analyses for models and tooling, establishing AI governance for risk, privacy, bias, and compliance, advising the CEO and executive leadership on frontier AI developments, and maintaining a rolling 6/12/24-month transformation roadmap with milestones and investment thresholds. 2. Architect and deliver production-grade AI systems at enterprise scale, including LLM-powered applications (RAG systems, agentic workflows, fine-tuning pipelines, prompt frameworks), AI-native infrastructure (model serving, evaluation, A/B testing, version control for prompts/models, continuous monitoring), and robust evaluation/benchmarking (hallucinations, task accuracy, latency, safety, user satisfaction). 3. Drive organizational AI enablement and adoption by designing company-wide AI literacy programs, creating internal tooling and playbooks (prompt libraries, no-code/low-code interfaces, copilots, AI-assisted workflows), establishing an AI Center of Excellence, implementing structured use-case intake/prioritization, building an AI talent strategy, and fostering a culture of responsible AI experimentation with post-mortems and knowledge sharing.