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Head of Artificial Intelligence

Core Strategic Responsibilities Defining AI Strategy & Vision: Building a long-term enterprise roadmap that aligns AI initiatives with overarching business goals, such as revenue growth, cost reduction, and operational efficiency. Portfolio Management: Identifying and prioritizing high-impact use cases (e.g., generative AI agents, predictive analytics) based on ROI, feasibility, and risk. Executive Evangelism: Acting as the "face" of AI for the organization, educating the board and external stak

Morgan McKinley - Hong Kong - Full time

Salary: Competitive

Core Strategic Responsibilities
  • Defining AI Strategy & Vision: Building a long-term enterprise roadmap that aligns AI initiatives with overarching business goals, such as revenue growth, cost reduction, and operational efficiency.
  • Portfolio Management: Identifying and prioritizing high-impact use cases (e.g., generative AI agents, predictive analytics) based on ROI, feasibility, and risk.
  • Executive Evangelism: Acting as the "face" of AI for the organization, educating the board and external stakeholders on AI's potential and limitations.
Governance and Risk Management
  • Ethical AI Frameworks: Establishing policies for FATE (Fairness, Accountability, Transparency, and Explainability) to mitigate algorithmic bias and ensure responsible use.
  • Regulatory Compliance: Navigating complex legal landscapes, such as the EU AI Act and national executive orders, to ensure all deployments meet data privacy and security standards.
  • Model Risk & Security: Partnering with CISOs to oversee the security of AI models and data pipelines against breaches or misuse.
Implementation and Operations
  • Cross-Functional Leadership: Coordinating between IT, engineering, legal, HR, and marketing to integrate AI seamlessly into existing workflows rather than leaving it in silos.
  • Talent & Culture: Recruiting top-tier AI talent and spearheading organization-wide upskilling programs to build an AI-literate workforce.
  • Infrastructure Oversight: Evaluating and selecting AI tools, vendors, and cloud platforms to build scalable MLOps and data environments.
Performance Monitoring
  • KPI Tracking: Establishing metrics to measure AI success, such as model accuracy, time-to-deployment, and demonstrable business value (ROI).
  • Scaling Pilots: Managing the transition of AI projects from experimental prototypes to full-scale production environments.
Requirements
  • Postgraduate degree holder in a relevant discipline
  • At least 15 years of relevant experience gained in large organisations
  • Fluent English and Cantonese


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