About Us

Headquartered in Singapore, SATS Ltd. is one of the world’s largest providers of air cargo handling services and Asia’s leading airline caterer. SATS Gateway Services provides airfreight and ground handling services including passenger services, ramp and baggage handling, aviation security services, aircraft cleaning and aviation laundry. SATS Food Solutions serves airlines and institutions, and operates central kitchens with large-scale food production and distribution capabilities for a wide range of cuisines.

SATS is present in the Asia-Pacific, the Americas, Europe, the Middle East and Africa, powering an interconnected world of trade, travel and taste. Following the acquisition of Worldwide Flight Services (WFS) in 2023, the combined SATS and WFS network operates over 225 stations in 27 countries. These cover trade routes responsible for more than 50% of global air cargo volume. SATS has been listed on the Singapore Exchange since May 2000. For more information, please visit www.sats.com.sg

Why Join Us

At SATS, people are our greatest asset and we build our success on the knowledge, expertise and performance of every contributor, by embracing diversity and uniqueness. As part of our holistic approach and commitment to embracing FAM (Fulfilling, Appreciated, Meaningful) in the workplace, we offer the runway to develop Fulfilling careers that foster your career growth, recognising and Appreciating the strength of talent and capabilities that we continue to build internally; and inspiring and encouraging each other to make Meaningful contributions in the work we do at SATS.

Key Responsibilities

We are hiring a Head of AI Centre of Excellence (CoE) who is a senior technology and data leader responsible for building and scaling enterprise-wide AI, data, and advanced analytics capabilities. This role defines and executes the organization’s AI platform strategy, enabling scalable, production-grade AI/ML systems that drive business transformation across operations, logistics, and customer solutions.This position will report directly to the SVP, Data & AI and play a critical role in shaping the enterprise AI foundation.

Key Responsibilities

AI Strategy & CoE Leadership

• Build and lead the AI CoE, establishing enterprise-wide AI vision, strategy, and execution roadmap with SVP of Data and AI.
• Serve as the principal authority on AI/ML platforms, advanced analytics, and intelligent systems
• Define and execute multi-year AI and data platform strategy aligned to business objectives
• Work very closely with SVP, Data & AI to shape long-term AI innovation and adoption strategy
• Drive enterprise AI transformation across operations, customer solutions, and corporate functions

AI Platforms & Engineering

• Design and implement scalable AI and data platforms (batch, streaming, real-time)
• Lead development of AI services, tools, and reusable frameworks
• Build and operationalize MLOps, LLMOps, and model lifecycle management capabilities
• Enable self-service AI/ML capabilities for engineering and data science teams
• Establish simulation, experimentation, and model validation platforms
• Ensure scalability, reliability, performance, and cost-efficiency of AI systems
• Drive standardization of AI engineering practices, tooling, and governance

Advanced AI Capabilities & Innovation

• Lead development of enterprise AI use cases including:
  • Forecasting and demand planning
  • Optimization and operations research (MIP, LP)
  • Simulation and scenario modeling
  • Root cause analysis and diagnostics
  • Recommendation systems
  • GenAI, agents, and agentic workflows
  • Vision computing and intelligent automation
• Champion adoption of next-generation AI technologies including GenAI, multi-agent systems, and autonomous decisioning platforms
• Embed AI into frontline operations and business processes

Data & AI Architecture

• Define scalable, secure, and efficient data and AI architecture
• Build robust data pipelines, feature stores, and model serving infrastructure
• Ensure alignment between data platforms and AI systems
• Drive improvements in data quality, accessibility, and governance
• Establish architecture patterns for reusable and composable AI systems

Cross-Functional Leadership & Execution

• Partner with Product, Data Science, Engineering, and regional teams to accelerate AI adoption
• Collaborate with business stakeholders to identify high-impact AI opportunities
• Translate business needs into scalable technical solutions
• Drive alignment across global teams and ensure consistent execution

Global Team Leadership & Development

• Build, mentor, and retain high-performing global AI and engineering teams
• Establish strong engineering and data science culture focused on excellence and innovation
• Create career development pathways and continuous learning programs
• Foster collaboration across geographies and functions
• Promote a culture of experimentation, ownership, and operational discipline 

Key Requirements

Leadership and Professional Experience

• Bachelor of Science / Master of Science / Doctor of Science, specialised in Computer Science, Data Science or related disciplines.
• Minimum 12 to 15 years of experience in data engineering, AI platforms, or distributed systems.
• Familiar and with proven experience in building and scaling AI platforms and data infrastructure.
• Experience in steering thought leadership in global aspects, across cross-functional teams.

AI / ML and Engineering Expertise

• Minimum 10 years of experience in building production-grade ML systems at scale.
• Strong hands-on experience with Python and ML frameworks (e.g., PyTorch, scikit-learn)
• Experience with GenAI frameworks, LLMs, fine-tuning, MoE architectures, and agents
• Hands-on experience building agentic systems and intelligent workflows

Platforms and Architecture

• Deep expertise in distributed systems, big data, and data pipelines
• Experience with streaming, ETL, simulation, and real-time platforms
• Strong experience in cloud platforms (AWS, Azure, GCP) and hybrid environments
• Experience with containerization (Docker, Kubernetes)

MLOps and Engineering Practices

• Strong experience with MLOps platforms (Airflow, Kubeflow, Dagster, etc.)
• Experience with CI/CD, DevOps, automated testing, and version control (GitHub)
• Proven ability to build scalable ML pipelines and deployment systems

Advanced Technical Depths

• Machine Learning and Deep Learning
• NLP and GenAI
• Optimization techniques (MIP, LP)
• Statistical analysis and data science
• Data engineering and big data technologies