1. Company AI Strategy & Transformation

• 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

• 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

• 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.

• 10+ years in AI / deep learning, with at least 5 years in a senior leadership role (Director+ or equivalent at a top-tier tech company, high-growth startup, or leading AI lab).
• Demonstrated track record of shipping production AI systems that directly impacted business outcomes at scale (revenue, engagement, efficiency).
• Deep expertise across the modern AI stack: large language models, transformer architectures, RAG, fine-tuning, RLHF/DPO, prompt engineering, and agentic AI frameworks.
• Strong publication record, open-source contributions, or recognized thought leadership in the AI community (conference talks, technical blog posts, courses, or widely-adopted tools).
• Exceptional communication skills: ability to present complex technical concepts to board-level audiences and translate business needs into technical specifications.
• Advanced degree (MS or PhD) in Computer Science, Artificial Intelligence, Statistics, or a related quantitative field.

• Experience in fintech, crypto/blockchain, or regulated industries with complex compliance requirements.
• Hands-on experience building AI-native products in consumer-facing, high-throughput environments (millions of daily active users).
• Track record of driving company-wide AI adoption initiatives — not just team-level, but organization-wide cultural and process transformation.
• Experience building or contributing to open-source AI tools, frameworks, or educational content adopted by the broader community.
• Familiarity with AI regulatory landscapes across multiple jurisdictions (US, EU AI Act, APAC frameworks).
• Published author or recognized educator in AI (books, widely-read technical blogs, MOOCs, or workshops).