Roles & Responsibilities
Design, prototype, and build ML models (e.g. recommendation, forecasting, NLP, computer vision)
Take models from proof-of-concept through to production deployment, including data pipelines, training workflows, and model serving infrastructure
Collaborate closely with HQ engineering and data science teams on model architecture, data standards, and shared infrastructure
Work with APAC product, business, and regional engineering teams to gather requirements and localize solutions
Monitor and maintain model performance in production, including retraining and iteration based on live feedback
Contribute to MLOps best practices, including CI/CD, model versioning, and experiment tracking
Communicate technical trade-offs clearly to both technical and business stakeholders across regions and time zones
Requirements
Bachelor's or Master's Degree in Computer Science, Machine Learning, Statistics, or a related field (or equivalent practical experience)
Around 3 years of experience building and deploying machine learning models in production environments
Strong proficiency in Python and ML frameworks such as PyTorch or TensorFlow
Solid understanding of the full ML lifecycle: data pipelines, feature engineering, training, evaluation, deployment, and monitoring
Experience with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes)
Strong communication skills and ability to work effectively across cross-border, cross-functional teams
Interested candidates, who wish to apply for the advertised position, please click on "Apply Now". We regret that only shortlisted candidates will be notified.
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