I engineer Dual-Stage Deep Learning Frameworks and take YOLO models from PyTorch to INT8-quantized edge inference. Obsessed with building scalable, user-centric digital experiences at the intersection of AI and Design.
const atharva = {
role: "Computer Science
Engineer (AI & ML)",
university: "Universal AI
University",
location: "Pune /
Pimpri-Chinchwad",
currently: "AI Intern @
RAMS Digital",
passions: ["Deep
Learning", "Edge AI", "Agentic
AI", "3D Design"]
};
// Bio
I am an innovator at heart. Currently pursuing my B.Tech and interning as an AI Intern at RAMS Digital, I live at the intersection of AI Innovation and Creative Design.
My journey involves building complex Dual-Stage Deep Learning Frameworks for medical diagnosis,
architecting autonomous systems for hospitals, and taking YOLO models from PyTorch through ONNX and
MLIR to INT8-quantized edge deployments.
Beyond code, I led the AI Odyssey 1.0 hackathon and actively
contribute to the tech community. I combine technical rigor with a designer's eye to create meaningful
digital solutions.
B.Tech in CSE (AI & ML)
Specializing in Deep Learning architectures, Neural Networks, and Autonomous Agents. Active participant in technical symposiums and hackathons.
Higher Secondary Certificate
Graduated with First Division Distinction. Built a strong foundation in Mathematics, Physics, and Computer Science fundamentals.
My contributions to the industry and leadership roles.
RAMS Digital, Pune Division
Sparge Chem Private Limited
AI Odyssey 1.0 @ Universal AI University
A selection of my recent R&D work and Full Stack applications.
Dual-stage deep learning framework for breast cancer histopathology, reaching 84.84% accuracy. Introspection layer using Entropy & Embedding Distance flags ~75% of uncertain predictions for human review.
Digital health record system for migrant workers. AI-driven patient surge prediction improving capacity planning by 35%.
Production CRO/CDMO platform for a custom-synthesis chemical manufacturer, with an RDKit/3Dmol-powered molecule search and catalog, admin automation, and a PostgreSQL backend on a self-hosted VPS.
YOLO-based rack/warehouse defect detection and real-time PPE compliance, quantized to INT8 and deployed on CVITEK NPU / MaixCAM edge hardware with no cloud dependency.
AI-powered system generating consultant-grade multi-sheet DXF/SVG drawing packages for water infrastructure, with a precedent library from 38 real WTP drawings across 9 projects.
Whether you have a question, a project idea, or just want to discuss the latest in AI, I'm always open to connecting.