Key Responsibilities
- participate in Machine Learning project lifecycle: problem scoping, data acquisition, feature engineering, model training, evaluation, deployment, and monitoring
- Develop and optimize machine learning models (classification, regression, clustering, ranking, recommendation) using Python and PyTorch
- Basic Understanding on feature engineering pipelines and data preprocessing workflows at scale
- Deploy models to production environments on cloud platforms using containerization
- Monitor model performance in production, detect drift, and implement retraining strategies
