Greater Delhi Area
Machine Learning Engineer with a strong foundation in Applied AI, Machine Learning, and Deep Learning. Experienced in building end-to-end ML pipelines, explainable AI (SHAP), and predictive models for healthcare and drug discovery using Python, TensorFlow, PyTorch, and Scikit-learn. Passionate about developing scalable, production-ready AI solutions and continuously expanding expertise in LLMs and MLOps.
•Trained deep learning models on large-scale ionospheric time-series data, improving anomaly detection accuracy for space weather prediction. • Built data pre-processing and feature engineering pipelines to handle noisy atmospheric datasets and temporal signals. • Conducted multiple TensorFlow experiments to evaluate model performance and optimize training stability. • Collaborated with research scientist to translate domain problems into scalable machine learning workflows.
• Built Azure ML regression models for global life expectancy prediction, achieving 0.968 R2 and 0.978 Spearman correlation. • Used Azure AutoML for feature engineering and hyperparameter tuning to optimize model performance. • Managed end-to-end ML workflow including data preprocessing, experiment tracking, evaluation (RMSE: 1.02, MAE: 1.07), and deployment.