Head of AI Engineering | Consultant Organization Tech Optimization | Principal AI Architect | Ph.D. in Computer Vision | GenAI & Multi-Agent Systems
Bridging deep academic research with enterprise-scale engineering. I architect Agentic AI systems and Deep Learning pipelines that move beyond POCs into production-grade, high-ROI innovation.
Principal Investigator for a COVID-19 thermal screening AI project, securing $3M+ in DST (Govt. of India) and VC funding.
Architected LLM-powered automation platforms for 50+ global clients (including Bosch), delivering 40% operational efficiency gains.
Founder of Codeidea.in — competitive coding & hackathon platform with 500+ students. Acquired by IVISLabs Pvt. Ltd.
LangGraph, LangSmith, OpenClaw, PyTorch, Transformers, Kubernetes, AWS.
My Ph.D. thesis, Segmentation in Compressed Document Images (University of Mysore, 2019), forms the foundation of my applied work in visual AI and OCR pipelines.
A hierarchical multi-scale network processing fundus images across four parallel branches at different resolutions, combining Dice, BCE, and centerline Dice losses with hard example mining. Achieves mean Dice of 88.72%, Sensitivity 90.78%, and AUC 98.25% across DRIVE, STARE, and CHASE_DB1.
An empirical study across five datasets quantifying the structural loss of microvascular details during downsampling.
Published in IEEE Access, vol. 12, 2024. DOI: 10.1109/ACCESS.2024.3455433
Published in Journal of Intelligent & Fuzzy Systems (JIFS), vol. 36, no. 3, pp. 2527-2544, 2019.
Developed advanced OCR and NLP models reducing manual cataloguing effort by 70%+ for e-commerce pipelines at Amazon, Flipkart, and LiveAuctioneer.
15+ years defining AI strategy, system architecture, and product roadmaps for enterprise and government clients.
Understanding RAG Systems the Simple Way — A guide for those who prefer clarity over jargon.
Think of RAG like asking someone to look something up in a book before answering your question, instead of relying only on their memory.
RAG = Retrieval-Augmented Generation. In plain English: the AI reads your documents first, then answers your questions based on what's written there.
Especially useful if you're managing family documents, medical records, financial papers, or business files.
Medical: Upload all your medical reports and ask "Has my cholesterol improved?" The system reads all your lab reports and gives you facts.
Financial: Upload your insurance documents and ask "What happens if I can't pay?" Find the exact clause in your policy.
Family Records: Upload grandchild's school deeds and ask "What are the academic requirements?" Get instant answers without calling.
You're no longer at the mercy of your memory. You have a system that reliably reads, understands, and retrieves information from your documents whenever you need it.
It's like having a patient librarian in your home who never gets tired, never forgets, and always tells you the source.
Learn More →See how Retrieval-Augmented Generation works in a real, live demonstration. Watch the system retrieve information from documents and generate answers.
In this demo, you'll see the complete RAG workflow: uploading a document, asking specific questions, and getting answers grounded in the actual content with source attribution.
🚀 Try it yourself: No installation required. Visit our live interactive demo on HuggingFace Spaces and practice with your own documents instantly.
Try Live Demo on HuggingFace Spaces →Interactive proof-of-concepts showcasing applied AI capabilities and engineering depth.
Autonomous research agents that retrieve, synthesize, and critique domain-specific documents.
LLM-powered orchestration for enterprise document processing with validation.
Interactive retinal vessel segmentation with hierarchical multi-scale attention.
End-to-end OCR and NLP pipeline for automated content extraction.
Real-time messaging, voice, and video platform optimized for low-latency communication.
Algorithm visualizations, real-time problem-solving environments for 500+ students.