Digital Transformation Technical Leader

ATOS INFORMATION TECHNOLOGY (SINGAPORE) PTE. LTD.; Singapore; On-site; SGD 250,000 - 320,000; Full time

Benefits offered by this job

Health insurance

Job summary

ATOS INFORMATION TECHNOLOGY (SINGAPORE) PTE. LTD. is seeking a visionary leader to define and execute a Singapore-based Data and AI strategy, shaping investment priorities across Generative AI, predictive AI, and automation.

You will guide architecture, governance, and enterprise data platforms while driving innovation through strategic partnerships. The role requires deep experience in AI advisory, real-world deployment, and governance, with a track record of leading multi-disciplinary teams

Qualifications

• Degree in Computer Science, Artificial Intelligence, or related field.

Responsibilities

• Define and execute the organization’s Data and AI strategy, roadmap, operating model, and investment priorities.
• Lead the design, development, testing, deployment, and scaling of enterprise AI solutions.
• Establish governance, safety, privacy, and accountability for Responsible AI throughout the lifecycle.
• Build partnerships with universities, government agencies, and technology partners to advance AI research and capability-building.
• Provide strategic oversight of enterprise data architecture, governance, quality, security, and analytics.

Skills

AI strategy

Data governance

Machine learning

Generative AI

RAG

Python

Cloud platforms

Stakeholder management

Leadership

Research partnerships

Education

Bachelor's/Master's in CS/AI

Tools

Databricks

PyTorch

TensorFlow

MLOps

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.