Job Description

Primary Responsibilities:

• Build and maintain the ML platform (feature store, model registry, training/serving infrastructure)​
• Create standardized ML pipelines for training, evaluation, deployment, and monitoring​
• Package and optimize models for real‑time or batch inference​
• Implement MLOps best practices: CI/CD for ML, automated testing, versioning​
• Monitor model drift, data quality, performance degradation, fairness, and RAI compliance
• Integrate rollout patterns (blue/green, canary) and automated rollback​
• Work with Data Scientists on feature engineering, performance optimization, and evaluation
• Ensure feature reuse and dataset consistency across domains​
• Partner with Ops and Governance to meet SLOs, quality standards, and risk controls

Qualifications:

• Must be a graduate of Bachelor’s degree in Computer Science, Computer Engineering, Data Science, Software Engineering, or a related technical discipline.
• Master’s Degree in Machine Learning, Artificial Intelligence, Computer Science, or related field is an advantage. Relevant certifications in Cloud Platforms, Kubernetes, or MLOps practices are preferred.
• Minimum of five (5) years of experience in Machine Learning Engineering, MLOps, Platform Engineering, or Software Engineering roles with ML exposure.
• Proven experience building and maintaining end-to-end ML platforms including feature stores, model registries, and training/serving infrastructure.
• Hands-on experience productionizing ML models into scalable batch and real-time inference environments. 
• Experience implementing CI/CD pipelines for ML workflows, including automated testing, model validation, and version control.
• Experience monitoring model performance in production, including drift detection, bias monitoring, data quality validation, and performance degradation analysis.
• Must have a valid work visa or legal authorization to work in the Philippines.