ML Engineer - Supply Chain AI

SPACE EXECUTIVE PTE. LTD.; Singapore; On-site; SGD 90,000 - 180,000; Full time

Job summary

SPACE EXECUTIVE PTE. LTD. is seeking an ML Engineer to bridge data science and production, shaping ML systems from the ground up. This hands‑on role sits at the intersection of ML engineering and platform infrastructure, with direct impact across Southeast Asia.

You will own deployment, build ML platform tooling, monitor models, and create retraining pipelines. Collaboration with data scientists and cloud‑based deployments (AWS/GCP/Azure) will be essential.

Qualifications

• 2–5 years of hands‑on experience in ML engineering or a closely related role.
• Practical experience deploying ML models into production — not just building or training them.
• Hands‑on experience with ML lifecycle tools — MLflow, Airflow, Kubeflow, SageMaker, Vertex AI, or similar
• Understanding of model monitoring and drift detection in production environments
• Comfortable with Docker, Kubernetes, and CI/CD pipelines
• Cloud experience on AWS, GCP, or Azure
• Strong Python skills with solid software engineering fundamentals

Responsibilities

• Own the deployment and operationalisation of ML models into production — building the infrastructure that takes models from development to live business systems
• Build and maintain ML platform tooling — experiment tracking, model versioning, model registry, and automated deployment pipelines using tools like MLflow and Airflow
• Implement model monitoring and drift detection to ensure production models stay accurate over time
• Build automated retraining pipelines so models stay relevant as data patterns change
• Work closely with data scientists to build self‑serve tooling that enables them to deploy and iterate on models independently
• Deploy and manage ML workloads on cloud platforms (AWS, GCP, or Azure) using Docker, Kubernetes, and CI/CD pipelines

Skills

Python

Docker

Kubernetes

CI/CD

MLflow

Airflow

Kubeflow

SageMaker

Vertex AI

Model monitoring

Drift detection

Cloud platforms

AWS

GCP

Azure

Software engineering

Tools

Git

Terraform

Helm

Job description

About The Company

Our client is an AI startup building intelligent supply chain solutions across Southeast Asia — from demand forecasting to inventory optimisation. They're a lean, engineering-first team that moves fast and gives engineers real ownership over the products they build.

The Role

They're looking for an ML Engineer to bridge the gap between data science and production — taking models built by their data science team and operationalising them into reliable, scalable systems. This is a hands‑on role sitting at the intersection of ML engineering and platform infrastructure.

This is a greenfield opportunity — you'll be shaping how they build and operate ML systems from the ground up, with direct impact on a product used by major retailers across SEA.

What You'll Do

• Own the deployment and operationalisation of ML models into production — building the infrastructure that takes models from development to live business systems
• Build and maintain ML platform tooling — experiment tracking, model versioning, model registry, and automated deployment pipelines using tools like MLflow and Airflow
• Implement model monitoring and drift detection to ensure production models stay accurate over time
• Build automated retraining pipelines so models stay relevant as data patterns change
• Work closely with data scientists to build self‑serve tooling that enables them to deploy and iterate on models independently
• Deploy and manage ML workloads on cloud platforms (AWS, GCP, or Azure) using Docker, Kubernetes, and CI/CD pipelines

What We're Looking For

• 2–5 years of hands‑on experience in ML engineering or a closely related role
• Practical experience deploying ML models into production — not just building or training them
• Hands‑on experience with ML lifecycle tools — MLflow, Airflow, Kubeflow, SageMaker, Vertex AI, or similar
• Understanding of model monitoring and drift detection in production environments
• Comfortable with Docker, Kubernetes, and CI/CD pipelines
• Cloud experience on AWS, GCP, or Azure
• Strong Python skills with solid software engineering fundamentals

Bonus points for:

• Experience with supply chain AI — demand forecasting, inventory optimisation, or similar
• Familiarity with traditional ML models (time‑series forecasting, gradient boosted trees, optimisation) rather than purely GenAI/LLM work
• Experience with constraint programming tools like Google OR-Tools
• Infrastructure as code experience (Terraform, Helm)