Forward Deployed Senior Engineer, AI & Analytics

Worldwide Flight Services Holland; Singapore; In loco; SGD 231.000 - 307.000; Tempo pieno

Descrizione del lavoro

SATS is building its data and AI capability across the Americas, and you will be the Forward Deployed Engineer, the technical counterpart to business product owners, embedded with operations, and responsible for delivering flagship AI and analytics initiatives from first implementation through station rollout.

This hands-on engineering role writes production Python code against live data, supports go-live, and remains accountable for the solution in service while driving adoption and measurable

Competenze

• 6+ years in software or ML engineering, including customer-facing delivery.
• Experience shipping complex systems end-to-end in ambiguous, fast-moving environments.
• Production experience with LLM/ML systems: prompt and agent design, RAG, evaluation frameworks, deployment at scale.
• Strong Python and API development (FastAPI or equivalent); capable of building a working system end-to-end.
• Practical depth in Docker, Kubernetes, CI/CD, and cloud data platforms; batch and real-time integration against messy enterprise sources.
• Fluency in the analytics side: semantic modelling, BI, and data products for non-technical users.

Mansioni

• 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; participate in stand-ups and on‑the‑floor activity to define local data reality.
• Ship production code in Python against live data and support go-live.
• Scale deployment and reduce time-to-deploy across stations with reusable integrations and runbooks.
• Own adoption and benefits; track usage and report against business case.

Conoscenze

Python

API development

Docker

Kubernetes

CI/CD

LLM/ML engineering

Formazione

Bachelor's degree in Computer Science/Engineering

Strumenti

FastAPI

Cloud platforms

Databases

Descrizione del lavoro

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

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

Measured movement against the business case, evidenced not asserted

Self-service

Users answering their own questions; standing report requests reduced

Demonstrable reduction per additional station

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.