Data Science and Machine Learning professional specializing in NLP, deep learning architectures, and GenAI workflows. Hands-on experience building, optimizing, containerizing, and deploying functional applications using RAG frameworks, transformers, Docker, and automated APIs.
End-to-end solutions spanning Generative AI, Deep Learning, ETL, and Analytics.
Developed an end-to-end RAG system for legal document QA, clause retrieval, and summarization.
Built an NLP pipeline leveraging BERT and deep learning models to classify sentiment from texts.
Architected an analytics application using Python and Streamlit to evaluate Campaign performance.
Engineered a robust ETL pipeline fetching live data via AeroDataBox API. Automated transformation and cleaning workflows, loading refined datasets into a relational database for real-time querying.
Developed machine learning models to predict patient no-shows using demographic and health factors. Conducted EDA and feature engineering to discover key predictors influencing attendance behavior.
Applied Unsupervised Machine Learning techniques including Clustering, Anomaly Detection, and Principal Component Analysis (PCA) to evaluate Public Distribution System datasets.