Job Responsibilities
Commercial Responsibilities
The commercial focus is entirely about value creation, revenue growth, and strategic alignment with business KPIs.
- AI Monetization & Strategy: Define and execute the corporate AI strategy to unlock new revenue streams, optimize pricing strategies, and monetize data assets.
- ROI & EBITDA Delivery: Own the financial targets (like EBITDA improvements) derived from AI projects, ensuring initiatives deliver tangible financial impact.
- Portfolio & Product Management: Build and manage a prioritized portfolio of AI use cases aligned with divisional goals, bridging technology with the product roadmap.
- Vendor & Partner Management: Evaluate, negotiate with, and manage external AI vendors, cloud providers, and strategic partners to accelerate time-to-market while minimizing costs.
- Change Management & Literacy: Build AI literacy across non-technical business units, driving the adoption of AI tools among sales, marketing, and operations teams.
- Market Intelligence: Monitor industry trends and competitor landscapes to ensure the company retains its competitive edge using emerging technologies.
Technical Responsibilities
The technical focus centers on architecture, infrastructure scalability, and leading the technical delivery teams.
- Technical Roadmap & Architecture: Architect full Machine Learning (ML) platforms and define the core infrastructure required to support scalable AI solutions.
- Team Leadership & Engineering: Provide collaborative leadership to recruit, mentor, and build out multidisciplinary teams, including Machine Learning Engineers (MLEs), Data Scientists, and Data Engineers.
- Lifecycle & Operations (MLOps): Define best practices for the AI/ML lifecycle, overseeing data engineering pipelines, model training, optimization, and continuous deployment.
- Data Excellence & Governance: Drive data quality and governance frameworks across the organization, ensuring data architecture can support advanced modeling.
- R&D and Prototyping: Oversee technical research, proof-of-concepts (PoCs), and internal pilots to test the viability of cutting-edge AI frameworks (e.g., Generative AI, LLMs) before scaling them.
Cross-Functional & Governance Responsibilities
This hybrid role acts as the ultimate translator between engineering code and executive boardrooms.
- Stakeholder Communication: Translate highly complex technical capabilities into clear, value-driven business language for executive stakeholders and clients.
- Ethics, Risk & Compliance: Ensure all deployed AI models adhere to strict data privacy laws (like GDPR/CCPA), cybersecurity protocols, ethical standards, and industry-specific regulations.
Job Requirements
- Bachelor's degree holder from a globally recognised university
- A minimum of 15-20 years of relevant experience gained from large companies
- Fluent Cantonese, English and Mandarin
