Principal Machine Learning Engineer

Principal Machine Learning Engineer with 13+ years designing, training, evaluating, and operationalizing classification, regression, ranking, and forecasting models for production applications. Deep hands-on expertise in Python, scikit-learn, pandas, NumPy, PyTorch, and TensorFlow, grounded in supervised and unsupervised learning, feature engineering, model evaluation, and statistical fundamentals. Proven record building reproducible training and validation pipelines, experiment tracking and model registries with MLflow, and model-serving components delivered as REST APIs and batch inference workflows across AWS, GCP, and Azure. Skilled at drift detection, production monitoring, model explainability (SHAP), and bias evaluation, with measurable gains in accuracy, latency, and cost. Trusted partner to data science, software engineering, and product teams in iterative Agile delivery, translating model trade-offs into clear engineering decisions.