
I design and deploy end-to-end ML solutions across healthcare, finance, and operations transforming complex data into actionable intelligence.
I am a Machine Learning Engineer with a strong foundation in mechanical engineering and a proven track record of building and deploying data-driven solutions across business, healthcare, and operational domains. My work focuses on developing scalable machine learning systems that transform complex data into actionable intelligence. I have designed and implemented high-performing models including fraud detection systems, medical risk predictors, forecasting models, and computer vision classifiers using modern frameworks such as TensorFlow and PyTorch.
Deep learning & classical ML
Pipelines & feature engineering
Docker, Kubernetes, CI/CD
98% accuracy, 0.94 AUC

Built and deployed an XGBoost Regression model to predict NYC Uber fares with high accuracy.

Built and evaluated a Decision Tree Classifier for fraud detection with class imbalance handling.

Multi-class sentiment analysis pipeline with extensive model benchmarking and optimization.

Transfer learning image classifier using InceptionV3 for binary classification.
Developed and deployed ML solutions across business, engineering, healthcare, and operations, automating workflows and improving forecasting accuracy.
Managed end-to-end contract execution in a high-paced printing press, introducing ML solutions to boost efficiency and client satisfaction.
Have a project in mind or want to discuss? I'm always open to meaningful conversations and exciting opportunities.