AI & Data Science undergraduate specializing in Deep Learning, NLP, and Retrieval-Augmented
Generation (RAG) systems. Experienced in end-to-end model deployment using PyTorch, ONNX
Runtime, and quantized LLMs with production implementations across on-device inference,
semantic vector search, and real-time voice pipelines. First Runner-up, Bengaluru AI Hackathon
CoComply AI
Bengaluru, Karnataka
Developing backend infrastructure and AI capabilities for Agent Coco, a multi-tier compliance AI assistant built on AWS Bedrock.
Building RAG pipelines, semantic search, knowledge ingestion, and vector DBs for LLM-powered compliance automation.
B.Tech
CGPA - 9.35
Built a fully offline Android AI assistant using quantized LLaMA via llama.cpp + JNI, achieving 4 ms inference latency with no network dependency.
Integrated ONNX Runtime, Room DB, and voice (STT/TTS) for end-to-end interaction.
Implemented persistent RAG pipeline using MiniLM embeddings and FAISS, enabling semantic memory recall across sessions entirely on-device.
Implemented neural style transfer using VGG19 feature maps and Gram matrix style loss, achieving high-fidelity stylization in 300 iterations on GPU.
Separated content and style representations from different network layers.
Applied layer-wise weighting to control style intensity.
Deployed as a web demo using HTML/CSS/JS for anyone to use.
Built an AI voice assistant on Android targeting 300M+ low-literacy Indians, supporting 8 auto-detected Indian languages with sub-second STT/TTS response on mid-range devices.
Shipped live screen assist using Android MediaProjection API floating voice orb with Llama 4 Scout vision, FLUX.1-schnell image generation, and real-time voice inference via Groq Llama 3.3 70B + Sarvam STT/TTS, all running zero cloud dependency for core features.
Engineered on-device RAG pipeline using ObjectBox vector DB, ONNX Runtime and MiniLM embeddings for persistent semantic memory.
NPTEL
Sapthagiri NPS University