AI Graduate Student RAG & Agentic Systems
Developed Sourava, a privacy-focused iOS voice-memory assistant featuring on-device speech transcription, semantic search, vector retrieval, and RAG-based conversational AI for intelligent memory recall and productivity workflows.
Experienced in building retrieval-augmented generation (RAG) pipelines, autonomous agent workflows, and conversational AI systems using Python, OpenAI APIs, Firebase, and MongoDB. Skilled in designing scalable AI architectures involving vector embeddings, semantic search, NLP pipelines, geospatial retrieval, and cloud-based automation.
Built AI-powered platforms including ride-sharing systems, event networking assistants, recommendation engines, and edge AI applications deployed on Raspberry Pi devices. Passionate about developing production-ready, context-aware AI systems that combine intelligent automation with real-world usability.
iOS voice-memory app with on-device Whisper transcription, semantic indexing (Core ML + vector store), and RAG Q&A over personal recordings. Built with Swift/SwiftUI; features include scheduling tools, lecture-to-PDF Classroom mode, and speaker-aware transcripts. Privacy-oriented, local-first design with optional OpenAI integration.
Built a multi-agent, AI-driven ride-sharing system with dual Telegram bots enabling conversational ride booking through OpenAI-powered NLP. Engineered a retrieval-augmented geospatial matching pipeline using MongoDB 2dsphere and Firebase Cloud Tasks, integrating RAG-based context retrieval and autonomous agent coordination for real-time scheduling and dynamic ride optimization.
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Developed an intelligent event networking bot powered by multi-agent collaboration and RAG pipelines to connect attendees at hackathons or conferences based on shared interests and extracted resume skills. Implemented seamless onboarding via event-specific QR codes and unique event IDs, enabling real-time participant matching, AI-driven team formation, and context-aware engagement throughout the event lifecycle.
Built a sandwich recommendation engine using voice input and daily retraining for improved accuracy.
Deployed a model on a Raspberry Pi to classify 200+ drone types from camera input using YOLO.
Created a disease prediction chatbot with 88% accuracy and published research on fine-tuned BERT models.
View IEEE PublicationMaster of Science, Artificial Intelligence
Open to collaborations, research, and AI engineering opportunities.
