Digital Transformation Technical Leader

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.