Sujith Chand

AI/ML Engineer and Computer Science undergraduate with hands-on experience in Computer Vision, Generative AI, and Retrieval-Augmented Generation (RAG). Proficient in Python, PyTorch, OpenCV, YOLOv5/YOLOv8, LangGraph, LangChain, and Weaviate. Built and deployed real-time computer vision pipelines achieving 99.7% accuracy and architected multi-agent RAG systems with 40% improved retrieval relevance.

Experienced in LLM fine-tuning, prompt engineering, and agentic workflow automation. Published researcher with industry internship experience.

Computer Vision Engineer Intern - Aytasense Technologies Pvt Ltd - Remote

(2024-08 - 2024-12)

• Developed and deployed YOLOv5 and YOLOv8-based real-time eye image detection pipeline using Python and OpenCV for computer vision applications in biometric and medical imaging.
• Applied CLAHE preprocessing, data augmentation, and model fine-tuning on medical datasets, improving iris-pupil detection accuracy from 92% to 99.7%.
• Optimized model architecture and inference pipeline for edge deployment, reducing model size by 35% while maintaining detection accuracy.
• Contributed to a Scopus Q2 research publication on real-time iris and pupil detection using YOLOv5 and YOLOv8 for biometric and medical applications.

AI Engineer Intern – Bharat Unnati AI Fellowship - LearnersByte Global InfoVision - Remote

(2026-01 - 2026-05)

• Explored Generative AI, LLM fine-tuning, prompt engineering, and automation workflows using ChatGPT, Gemini, and Claude APIs.
• Designed and deployed an Agentic AI workflow for Automotive CRM automation using n8n with integrated LLM processing nodes, reducing manual data entry time by 60%.
• Automated customer data handling, lead scoring, and CRM updates through JSON-based workflow integration, improving pipeline efficiency across 3 process stages.

B.E. - Computer Science - RV Institute of Technology and Management (2022 - present)

Intermediate - Bellari Independent PU College (2020 - 2022)