Machine Learning Engineer (12-Month Contract)

About LINE MAN Wongnai

We are Thailand’s leading provider of On-Demand Services (ODS), Merchant Digital Solutions(MDS), and Payment and Financial Services (PFS). We build technology to help Thai people live better, to empower all local businesses by creating an end-to-end food ecosystem through our channel. Connected consumers, riders, and local businesses and improved the daily life of all parties with restaurants nationwide. And because we are local, we provide the deepest variety and services that are tailor-made for Thai people.

About the Role

This is a 12-month, full-time contract engagement supporting a major migration of our machine learning and production model infrastructure to a modernized cloud environment. You'll embed directly with our Machine Learning Engineering and Data Science teams for the duration of the project, re-platforming existing training, serving, and feature data pipelines onto new cloud-native infrastructure with minimal disruption to production models. This is a hands-on execution role scoped to the migration; we need someone who can ramp quickly and start contributing within the first few weeks, rather than grow into long-term roadmap ownership.

What you’ll Do:

• Re-platform the ML Lifecycle: Migrate existing MLOps infrastructure, training pipelines, and CI/CD deployment workflows onto the new cloud environment's managed ML services, minimizing downtime and model drift during cutover.
• Drive Application Integration: Migrate APIs, microservices, and backend business logic connecting ML models to downstream applications, keeping prediction-serving SLAs intact through the migration.
• Migrate Data & Feature Pipelines: Re-platform data pipelines and feature stores onto the new cloud environment so training and real-time inference data flows continue uninterrupted.
• Own Production Health & Observability During Cutover: Establish monitoring, anomaly detection, and incident response for data quality, model drift, and A/B testing performance across both the current and new environments during the transition.
  Optimize System Performance Post-Migration: Troubleshoot production bottlenecks and tune models for latency, throughput, and compute cost efficiency (GPU/CPU utilization, inference cost management).
• Document & Hand Over: Produce migration runbooks and architecture documentation, and hand over ownership of migrated systems to the full-time team ahead of contract close.

What you’ll Need:

• 3+ years of professional experience in software engineering, machine learning engineering, or data science, with the ability to work independently from day one; this is a fixed-term engagement with limited onboarding runway.
• Working knowledge of core ML concepts and frameworks (PyTorch, TensorFlow, or Scikit-Learn), enough to validate that migrated models and pipelines produce correct output post-cutover.
• Strong proficiency with Python or Go in production environments, with exposure to distributed computing frameworks like Apache Spark considered a plus.
• Solid understanding of systems architecture and distributed systems, with the ability to maintain and re-platform complex ML pipelines and application logic against a defined timeline.
• Prior experience migrating infrastructure or workloads between environments (on-prem to cloud, or cloud to cloud) is a strong plus.
• Practical understanding of how MLOps pipelines interlock end to end, so migrating one component means tracing and moving everything upstream and downstream that depends on it.
• A pragmatic, high-velocity engineering approach, comfortable delivering against a fixed scope and timeline and balancing rapid delivery with a clean handover at contract end.
  

It'd be Great if you Have:

• Hands-on experience with major cloud platforms' ML and data services (for example AWS SageMaker, Glue, and EKS; GCP Vertex AI; or Azure ML).
• Experience with container orchestration tools like Kubernetes, or workflow orchestration tools like Apache Airflow, for managing scalable ML workloads.