Machine Learning Engineer (Hybrid)
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Summary
Machine Learning Engineer working hybrid from Mandaluyong City: researches, trains, optimizes, and deploys scalable ML models into production, collaborates with data engineers on datasets and feature pipelines, and monitors deployed model performance and drift. Core stack: Python or R, TensorFlow/PyTorch/Scikit-learn, and cloud ML platforms like AWS SageMaker.
Machine Learning Engineer
- Role Summary: Responsible for researching, training, optimizing, and deploying scalable machine learning models into production systems.
- Key Responsibilities:
- Build, train, and evaluate ML algorithms for performance, accuracy, and scalability.
- Collaborate with data engineers and scientists to prepare datasets and feature pipelines.
- Monitor deployed model performance, drift, and operational health.
- Qualifications:
- 3–5 years of practical experience in ML engineering and model deployment.
- Proficiency in Python or R and frameworks like TensorFlow, PyTorch, or Scikit-learn.
- Experience with cloud ML platforms (AWS SageMaker, Google AI Platform, or equivalent).
- Location & Setup: Hybrid – Mandaluyong City
