Key Responsibilities
Data and AI Strategy
- Define and execute the organization's Data and AI strategy, roadmap, operating model, and investment priorities.
- Identify high-value opportunities for Generative AI, Predictive AI, Agentic AI, data analytics, and automation.
- Advise senior management on emerging AI technologies, industry trends, risks, and strategic opportunities.
- Establish measurable outcomes and value-realization frameworks for Data and AI initiatives.
AI Solution Development and Delivery
- Lead the design, development, testing, deployment, and scaling of enterprise AI solutions.
- Provide technical direction for machine learning, Generative AI, retrieval-augmented generation, AI agents, and intelligent automation.
- Guide teams in selecting appropriate data platforms, models, AI frameworks, infrastructure, and development tools.
- Promote rapid prototyping and experimentation while ensuring that successful solutions can transition into secure production environments.
- Establish appropriate engineering, testing, evaluation, monitoring, and model lifecycle management practices.
Responsible AI, Safety and Governance
- Establish and maintain policies and controls for Responsible AI, AI safety, privacy, security, transparency, fairness, and accountability.
- Embed AI trust and safety requirements throughout the solution development lifecycle.
- Oversee AI risk assessments, model evaluations, human oversight mechanisms, and regulatory compliance.
- Define governance standards for enterprise AI agents, models, data, tools, and third-party AI services.
Research, Innovation and Ecosystem Partnerships
- Build strategic relationships with local universities, research institutions, government agencies, start-ups, and technology partners.
- Evaluate research findings and determine their potential for practical application and commercialization.
- Lead technical due diligence for AI technologies, platforms, research proposals, partnerships, and investment opportunities.
- Support the development of AI research, innovation, grant, and capability-building programmes.
- Represent the organization in relevant industry, government, and research forums.
Data Leadership
- Provide strategic oversight of enterprise data architecture, governance, quality, security, integration, and analytics.
- Ensure that data assets are reliable, accessible, well-governed, and suitable for AI development.
- Promote responsible data sharing and collaboration while protecting sensitive and regulated information.
- Work with technology and business teams to establish scalable data and AI platforms.
Team and Stakeholder Leadership
- Build, lead, and develop a multidisciplinary team of data scientists, AI engineers, data engineers, architects, researchers, and programme professionals.
- Establish technical standards, delivery practices, capability-development plans, and communities of practice.
- Communicate complex Data and AI concepts clearly to technical teams, business stakeholders, senior management, and external partners.
- Promote a culture of innovation, collaboration, responsible experimentation, and continuous learning.
Requirements
Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science
15+years of experience in pre-sales, management consulting, enterprisearchitecture, AI advisory, or customer-facing strategy roles.
- Strong AI literacy with a sound understanding of Generative AI, machine learning, agentic systems, and modern AI development practices.
- Several years of hands-on experience designing, building, and deploying AI solutions in real-world environments.
- Demonstrated experience leading Data and AI programmes, technical teams, research initiatives, or enterprise transformation projects.
- Practical knowledge of AI safety, security, ethics, trust, governance, and Responsible AI.
- Experience applying AI within sectors such as government, legal, healthcare, financial services, research, or other regulated industries.
- Experience collaborating with local research ecosystems, universities, government agencies, and technology partners.
- Strong analytical skills, including the ability to conduct technical due diligence, evaluate emerging technologies, and interpret research findings.
- Excellent written, verbal, presentation, and stakeholder-management skills, with the ability to explain complex technical subjects to both technical and non-technical audiences.
- Experience developing or managing research grants, innovation programmes, or similar technology initiatives would be advantageous.
- Hands-on experience with AI infrastructure or hardware benchmarking, Agentic AI, multi-agent systems, robotics, or embodied AI would be highly desirable.
- Generative AI, large language models, RAG, prompt engineering, fine-tuning, and model evaluation.
- Databricks Product Certification preferred.
- Agentic AI, tool calling, workflow orchestration, multi-agent systems, and human-in-the-loop controls.
- Python and common AI/ML frameworks such as PyTorch, TensorFlow, scikit-learn, or equivalent technologies.
- Cloud-based data and AI platforms, enterprise data architecture, MLOps, LLMOps, and model monitoring.
- Data governance, cybersecurity, privacy, AI regulations, and Responsible AI frameworks.
- AI compute infrastructure, GPUs, performance benchmarking, and workload optimization.
