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I build intelligent systems, RAG pipelines, and predictive models. Oracle Certified Generative AI Professional focused on turning complex data into scalable, production-ready applications.
I am a Computer Science undergraduate (B.Tech '26) specializing in Generative AI, Machine Learning, and Python Backend development. I have hands-on expertise in building end-to-end ML lifecycles, from data preprocessing and feature engineering to model deployment and MLOps.
Currently, I lead AI data generation initiatives and manage remote technical workflows, bridging the gap between creative strategy and technical execution. I am passionate about prompt engineering, vector search, and cloud-native development on Oracle Cloud Infrastructure (OCI).
A lightweight, production-ready Text-to-Speech application leveraging modern neural TTS models. Features efficient audio synthesis, customizable voice parameters, and a clean interface for seamless integration into broader AI and accessibility workflows.
End-to-end ML pipeline using Random Forest & Gradient Boosting. Features robust preprocessing via Scikit-Learn ColumnTransformer, k-fold cross-validation, GridSearchCV optimization, and unsupervised feature extraction (PCA, K-Means).
Engineered a robust Python scraping pipeline using yt-dlp and API pagination for bulk comment extraction. Processes unstructured data into normalized JSON/CSV formats for downstream NLP sentiment analysis workflows.
Built production-grade applications using LLMs, RAG, and Prompt Engineering. Integrated OCI Generative AI services, text embeddings, and inference APIs into Python backends.
Developed semantic search engines using vector embeddings and nearest-neighbor retrieval. Engineered RAG pipelines integrating vector databases with LLMs.