Worldwide Flight Services (WFS)

SATS Ltd Who we are 
 
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, 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 215 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 
 
The Role 
 
SATS is building its data and AI capability on two foundations: an AI Centre of Excellence in that establishes the platform, reference patterns and guardrails, and a Data Engineering and Platform function that makes enterprise data governed and trustworthy. 
 
We are looking for a Forward Deployed Engineer to work alongside these functions in the Americas, positioned close to the operation and accountable for the deployment, adoption and measured benefits of what we build. The role is the named technical counterpart to the business product owners in the region, embedded with the operations, and responsible for delivering flagship AI and analytics initiatives from first implementation through station rollout. 
 
This is a hands-on engineering role rather than an advisory one. You will write production code against live operational data, support users through go-live, and remain accountable for the solution in service. 
 
Key Responsibilities 
 
Deploy and land 
 

* Own end-to-end delivery of AI and analytics products at stations in your region: discovery, scoping, build, integration, UAT, hypercare and rollout.
* Embed with the operation. Sit in business stand-ups, walk the ramp and the cargo floor, and specify the local data reality rather than assuming it.
* Ship production code in Python and comparable stacks against live flight, cargo, workforce and equipment data, not prototypes handed to someone else to productionise.
* Scale deployment. Reduce cost and time-to-deploy from station N to station N+1 through reusable integrations, configuration and runbooks. 
 
 

Build on the platform, not around it 
 

* Consume through the semantic layer and the governed data platform. No parallel pipelines, no local definitions, no workarounds.
* Apply CoE patterns and guardrails: reference architectures, AI registry, human-in-the-loop controls, access and residency rules.
* Integrate GenAI and ML into workflows: RAG and retrieval, agentic and tool-use patterns, evaluation frameworks, observability and cost control in production. 
 
 

Make insight self-service 
 

* Build data products, not report queues. Design- governed, reusable datasets and semantic models that let operations, airlines and forwarders answer their own questions.
* Put insight in front of the customer. Deliver station and customer-facing analytics, including natural-language access to operational data, with the access, residency and confidentiality controls customer data demands.
* Design for the user, not the requester. Every product names the decision it changes and who makes it. Instrument usage, retire what nobody opens, consolidate the long tail rather than extend it. 
 
 

Close the loop 
 

* Feed the field back. Every integration problem you solve becomes a hardened pattern for the CoE and a requirement for the platform team.
* Own adoption and benefits. Instrument what you deploy, track usage and benefit against the business case, and report it honestly.
* Be the technical face to the business and to customers, translating between operations, the CoE and platform engineering. 
 
 

Success in the First 12 Months 
 
Measure 
 
What Good Looks Like 
 
Stations live 
 
Flagship AI and analytics products deployed and running at named sites in region 
 
Adoption 
 
Sustained use by the operational teams and customers the product was built for 
 
Benefit realized 
 
Measured movement against the business case, evidenced not asserted 
 
Self-service 
 
Users answering their own questions; standing report requests reduced 
 
Cost to deploy 
 
Demonstrable reduction per additional station 
 
Pattern reuse 
 
Field learnings hardened into CoE patterns others reuse 
 
Requirements 
 
Experience 
 

* 6+ years in software or ML engineering, including customer-facing or business-embedded delivery.
* Track record of scoping and shipping complex systems end to end in ambiguous, fast-moving environments, and staying accountable after go-live.
* Production experience with LLM or ML systems: prompt and agent design, RAG, evaluation frameworks, deployment at scale, and an understanding of how model behaviour shapes product experience.
* Strong Python and API development (FastAPI or equivalent); comfortable across backend and front-end enough to build a working system alone.
* Practical depth in Docker, Kubernetes, CI/CD, and cloud data platforms; batch and real-time integration against messy enterprise sources.
* Fluency in the analytics half of the job: semantic and dimensional modelling, modern BI, and designing data products that non-technical and external users can operate without help.
* Aviation, logistics or complex operations exposure is an advantage, not a prerequisite. Willingness to learn the operation from the floor is not optional. 
 
 

Qualifications 
 


* Bachelor's, MSc or PhD in Computer Science, Engineering, Mathematics or related field. 
 
 

Personal characteristics 
 


* Outcome ownership, measured on whether the operation changed, not on assets produced.
* Structured problem solving under ambiguity; able to decide with incomplete information and adjust.
* Credibility with both a ramp supervisor and a platform architect; clear written and verbal communication.
* Calm under operational pressure. Go-lives are rarely quiet. 
 
 

Working in Region 
 
You work alongside the regional Data Analytics & Insights team, station leadership and the business product owners for cargo and ground handling across the region. The role requires regular presence at stations in region and periodic travel to Singapore and India. Expect roughly 25-40% travel. 
 
Core Values 
 
Safety is ingrained in our DNA. Customer Focus, every smile is a win. Respect, we listen, understand, and respect. Excellence isn't just a goal; it's the SATS way. Teamwork, we don't just collaborate, we amplify each other's strengths.

Data

Data analytics

Security software

Python

Operations

System security

Embedded Systems

Wireframing/prototyping

Network

Software Development

Docker

Kubernetes

Problem solving

Data Engineering

Continuous Integration

Cloud

Mathematics

Support

Communications

Logistics

Fastapi

Machine Learning

Prompt Engineering

Reporting Frameworks

CI/CD

Cloud Computing Platforms

Semantic Web

Dimensional Modeling

Data Integration

Observability

Forward Deployed Senior Engineer, AI & Analytics