AI Engineer with 3+ years of R&D experience specializing in Computer Vision, Edge AI, and Large Language Models. I have a proven track record of architecting end-to-end machine learning pipelines, including a Dockerized …
AI Engineer with 3+ years of R&D experience specializing in Computer Vision, Edge AI, and Large Language Models. I have a proven track record of architecting end-to-end machine learning pipelines, including a Dockerized FinTech fraud detection system that achieved 98% accuracy in real-time document verification. My expertise spans optimizing deep learning models for edge devices like Jetson Orin Nano and developing multimodal LLM agents integrated with sensor data. I am highly skilled in bridging the gap between cutting-edge research and practical deployment using PyTorch, YOLO, TensorRT, and model quantization. As a published researcher, I am passionate about leveraging scalable ML infrastructure to build enterprise-grade applications and drive innovation
Architected an Immigration Document Verification System using Siamese Networks and YOLO, detecting fake passports via UV light pattern analysis with high precision., Engineered a Dockerized deployment pipeline, integrating OCR-free text extraction (Donut) and face comparison models for real-time identity verification., Optimized AI inference using model quantization, enabling efficient on-device performance for mobile and edge use cases., Authored technical research presented at ICGHIT 2025 regarding non-visible-light document verification.
Architected an Immigration Document Verification System using Siamese Networks and YOLO, detecting fake passports via UV light pattern analysis with high precision.
Engineered a Dockerized deployment pipeline, integrating OCR-free text extraction (Donut) and face comparison models for real-time identity verification.
Optimized AI inference using model quantization, enabling efficient on-device performance for mobile and edge use cases.
Authored technical research presented at ICGHIT 2025 regarding non-visible-light document verification.
Led the development of “Rumi”, a multimodal LLM agent integrated with EEG sensors for real-time human emotion detection and adaptive response generation.
Built and deployed a full-stack web application using Flask to serve the LLM model, ensuring scalability and real-time user interaction.
Developed a Text-to-Video generation pipeline using GANs, optimizing for high-fidelity output from natural language prompts.
Led the development of ”Rumi”, a multimodal LLM agent integrated with EEG sensors for real-time human emotion detection and adaptive response generation., Built and deployed a full-stack web application using Flask to serve the LLM model, ensuring scalability and real-time user interaction., Developed a Text-to-Video generation pipeline using GANs, optimizing for high-fidelity output from natural language prompts.
Developed an AI-powered passport verification system integrating an optimized YOLO model for document detection and Donut for OCR-free text extraction. Deployed a Siamese UV Sample Matching backend in Docker for authenticity verification under UV light, utilizing model compression for efficient on-device performance.Achieved a 98% accuracy rate in fraud detection
Led the development of "Rumi", a multimodal LLM agent integrated with EEG sensors for real-time human emotion detection and adaptive response generation. Built and deployed a full-stack web application using Flask to serve the LLM model, ensuring scalability and real-time user interaction. Developed a Text-to-Video generation pipeline using GANs, optimizing for high-fidelity output from natural language prompts.
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