• 1. Strategic Vision & Next-Gen Technology Roadmap
  • Formulate and drive the long-term applied research strategy, keeping the institute at the absolute cutting edge of LLM/LVLM, Agentic AI, and Generative AI.
  • Establish technology roadmaps tailored for high-impact sectors, primarily FinTech and Trusted Tech (Cybersecurity, Privacy-Preserving Computing), with cross-disciplinary expansion into Digital Health and Intelligent Manufacturing.
  • Champion AI enablement across all other Technology Divisions (TDs) to exponentially enhance collective research productivity and solution capabilities.
• 2. R&D Leadership & Platform Architecture
  • Lead, mentor, and scale a high-performing division of top-tier AI scientists, software architects, and engineers, fostering a culture of technical excellence.
  • Oversee the design, development, and standard setting of a robust, reusable, and scalable Core Base Agentic AI Platform that serves as the foundation for all vertical solutions.
  • Guide solution teams on modern system deployment architectures (Cloud, Containerization, MLOps Pipelines) to ensure all R&D outputs are highly feasible for real-world piloting and commercial integration.
• 3. Commercialization & Impact Acceleration (0 → 1 → 10)
  • Drive the technology transfer process, ensuring all applied research planning explicitly incorporates real-world business constraints, regulatory requirements, and customer feedback from the outset.
  • Partner closely with Business Development and Ecosystem teams to secure strategic pilot deployments, joint-labs, and commercial market validation.
  • Bridge the gap between advanced R&D and market readiness, defining clear paths to market for proprietary technologies.
• 4. High-Level Stakeholder & Ecosystem Management
  • Act as the ultimate technical ambassador to align complex multi-party interests across the technology ecosystem:
  • Government & Funding Bodies: Articulate the technical merit, strategic value, and macroeconomic impact of AI programs to secure public research grants.
  • C-Level Enterprise Clients: Deeply understand operational pain points of financial institutions and conglomerates, translating them into robust AI solutions.
  • Solution Integrators & Tech Vendors: Secure strategic partnerships to accelerate commercial adoption.

• Proven Leadership: 15+ years of experience in advanced technology research and solution development, with at least 8+ years in a senior executive role managing large-scale, multidisciplinary R&D teams within applied research institutes, corporate R&D hubs, or top-tier tech firms.
• Technical Depth: Outstanding technical credentials in AI/ML, with deep domain expertise in LLMs, GenAI, and Agentic AI architectures.
• Domain Expertise: A proven track record of successfully applying AI solutions within FinTech (e.g., algorithmic trading, credit scoring, fraud detection) or Trusted Tech (e.g., zero-knowledge proofs, federated learning, data governance).
• Deployment & Engineering Acumen: Solid understanding of modern production-level software architectures, including cloud-native infrastructures, containerization (Docker/Kubernetes), and continuous ML pipelines (MLOps/CI/CD).
• Commercial Success: Strong business acumen with a demonstrated track record of navigating the "0-to-1" to "1-to-10" product life cycle, converting lab research into commercially viable, scalable enterprise products.
• Stakeholder Management: Exceptional communication and presentation skills in English and Chinese (Mandarin/Cantonese). Experienced in navigating publicly funded, highly regulated, or academic-corporate hybrid ecosystems.

Interested parties please email a MS Word version resume and expected salary to  [email protected]  and quote the job reference no.