Yogesh User

Shipped AI-powered EdTech applications for customers across Singapore and Hong Kong. Built production cloud services with AWS, Docker, and Redis, and developed RAG/MCP-based agentic AI workflows with owned backend integrations and deployment pipelines.

Scaled an EdTech operations platform serving 2,500+ students across 5 schools in 4 Indian states. Integrated Razorpay, built multilingual AI voice-calling workflows, productized RFID/biometric attendance, and implemented hostel management, bus tracking, staff dashboards, and automated document generation modules.

Built a production handwritten script correction platform using TrOCR, RAG pipelines, and tuned Gemini models to reduce evaluation timelines. Engineered OCR/document intelligence pipelines, a RAG-based evaluation engine, teacher-in-the-loop workflows, and scalable backend deployment using FastAPI, Docker, PostgreSQL, Firebase, and GCP.

Built an end-to-end ML pipeline for crane risk prediction using Random Forest and XGBoost with approximately 90% predictive accuracy. Designed feature-driven evaluation and explainable risk scoring with rule-based safety constraints.

Co-authored and presented research on AI-driven academic assessment. Focused on using machine learning techniques for handwritten script evaluation.

Grade: GPA: 9.78/10

Activities and societies: Relevant coursework: AI, Machine Learning, Data Mining, Neural Networks, Blockchain, ML Mathematics.

Pursuing a B.Tech in Computer Science and Engineering (AI & ML), serving as the department topper (GPA 9.78/10).