Digital Transformation Technical Leader

ATOS INFORMATION TECHNOLOGY (SINGAPORE) PTE. LTD.; Singapore; On-site; SGD 250,000 - 500,000; Full time

Job summary

ATOS INFORMATION TECHNOLOGY (SINGAPORE) PTE. LTD. seeks a senior Data & AI leader to shape the company’s AI strategy and governance. You will drive enterprise AI solutions, define investments, and partner with universities and government bodies.

Lead data architecture, analytics, and cross-disciplinary teams to ensure scalable, trusted, compliant AI implementations across the organization.

Qualifications

• Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science
• 15+ years of experience in pre-sales, management consulting, enterprise architecture, AI advisory, or customer-facing strategy roles.
• Strong AI literacy with generative AI, ML, agentic systems, and modern AI development practices.
• Hands-on experience designing, building, and deploying AI solutions in real-world environments.
• Experience leading Data and AI programmes, technical teams, research initiatives, or enterprise transformation projects.
• Knowledge of AI safety, security, ethics, trust, governance, and Responsible AI.
• Experience collaborating with universities, government agencies, and technology partners.

Responsibilities

• 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, AI agents, data analytics, and automation.
• Advise senior management on AI technologies, trends, risks, and opportunities.
• Establish outcomes and value-realization frameworks for Data and AI initiatives.
• Lead design, development, testing, deployment, and scaling of enterprise AI solutions.
• Provide technical direction for ML, Generative AI, retrieval-augmented generation, and intelligent automation.
• Guide teams in selecting data platforms, models, AI frameworks, infrastructure, and tools.
• Promote rapid prototyping and secure production transitions.
• Establish ML lifecycle, monitoring, and evaluation practices.
• Establish governance standards for enterprise AI agents, models, data, tools, and services.
• Build strategic relationships with universities, research institutions, government agencies, startups, and partners.
• Evaluate research findings for practical application and commercialization.
• Lead technical due diligence for AI technologies, platforms, partnerships, and investments.
• Support AI research, innovation grants, and capability-building programs.
• Represent the organization in industry, government, and research forums.
• Provide strategic oversight of enterprise data architecture, governance, quality, security, integration, and analytics.
• Ensure data assets are reliable, governed, and suitable for AI development.
• Promote responsible data sharing while protecting sensitive information.
• Work with teams to establish scalable data and AI platforms.
• Build and mentor a multidisciplinary data/AI team; establish standards and communities of practice.
• Communicate complex Data and AI concepts to technical and non-technical stakeholders.
• Foster innovation, collaboration, responsible experimentation, and continuous learning.

Skills

AI strategy

Data governance

Stakeholder management

Team leadership

Analytical skills

Communication

Regulatory compliance

Risk assessment

Education

Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science

Tools

Python

PyTorch

TensorFlow

scikit-learn

Databricks

Job description

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.