Built and deployed an AI-powered music intelligence platform exploring the intersection of recommendation systems, personality, and music discovery. Developed a full-stack architecture using Python, Flask, React, MongoDB, REST APIs, Docker, and Spotify/Last.fm integrations to generate personalized music insights, embeddings-based recommendations, and interactive listening visualizations. Designed and integrated Auralith, an AI-driven music agent capable of analyzing listening behavior, explaining musical patterns, generating playlists, and creating personalized recommendation flows. Implemented ML pipelines for feature engineering, clustering, similarity modeling, and behavioral analysis using audio features, metadata, and listening signals across 10K+ music records. Engineered scalable backend APIs, authentication systems, modular frontend architecture, and real-time interactive visual systems including Galaxy maps, Soul Orbs, and music identity analysis workflows. Focused heavily on system architecture, recommendation quality, AI integration, observability, and production-style deployment workflows.