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 Lead AI engineering role for someone who wants to bring deep software architecture and cloud infrastructure experience into AI/ML and GenAI systems. You will design, build, and operate the platform that our production-grade AI/ML, GenAI, Simulation, sematic layer, knowledge base, RAG, OCR.

Expect to spend the majority of your time building: writing production code, architecting and provisioning cloud infrastructure, and debugging systems in production for GenAI — with technical mentorship and architecture reviews as a smaller part of the role, not the main one.

ML & GenAI Platform Engineering

• Design and build scalable ML/GenAI pipelines (batch & real-time)
• Build and operate model-serving infrastructure, profile and optimize inference latency in edge compute
• Build RAG pipelines, LLM orchestration frameworks, and multi-agent/agentic workflow systems
• Build and maintain vector database and embedding pipeline infrastructure
• Build and maintain company knowledge base
• Build and unify key components of AI Platform, e.g. API gateway, guardrail, auth enabled MCP/tool-calling, tracing
• Deep dive into best practice for context management, harness, agent memory management

MLOps & Reliability

• Implement model versioning, monitoring, and retraining pipelines
• Build integrated data flywheel for eval and model improvement
• Setup best practice for data versioning
• Ensure reproducibility and reliability of ML systems end-to-end

Software Architecture

• Make architecture decisions for the platform, then build the core pieces yourself as reference implementations others build on
• Implement shared libraries and reusable building blocks (self-service deployment, GenAI services/APIs) rather than just specifying them
• Review code and architecture for the rest of the team

Cloud & Infrastructure

• Architect and build the cloud infrastructure (AWS, Azure, or GCP) the platform runs on — compute, networking, IAM, storage, managed Kubernetes
• Write and maintain infrastructure-as-code (Terraform or equivalent)
• Build and maintain end-to-end CI/CD pipelines for ML/GenAI workloads
• Own cost and performance optimization of cloud and LLM infrastructure

Collaboration

• Work directly with data scientists and AI Engineers to productionize models
• Partner with the Head of AI on technical roadmap and cloud/vendor decisions
• Evaluate emerging GenAI tools and frameworks hands-on before recommending adoption

Key Requirements

• Degree qualifications at the Bachelor's, Master's, or PhD level in Computer Science, Mathematics, or a closely related field.
• With minimum 10 to 12 years in software engineering or GenAI Platform Engineering.
• Must-have: deep, hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) — experience in building and operating a production infrastructure
• Strong Python experience and hands-on expertise in: Docker, Kubernetes, Terraform/IaC, CI/CD pipelines, MLflow.
• Hands-on experience building and running Agentic AI applications/harnesses in production e.g. tool-calling agents, multi-agent orchestration, agent evaluation/observability.
• Ongoing commitment to training and professional development in AI/ML, GenAI, and the aviation domain — Cargo Handling, Ground Handling, Ground Freight, and Food Solutions.
• Experience in aviation, logistics, or supply chain industries.
• Familiarity with production Optimization (Operations Research), Computer Vision, or Simulation systems, OCR.