Sai Nivedh V
AI & Full Stack Developer
I'm a passionate AI & Backend Developer currently pursuing my bachelor's degree in Computer science with a specialization in Artificial Intelligence at Amrita Vishwa Vidyapeetham. With a strong foundation in various programming languages and application development principles , I'm dedicated to creating innovative solutions that push the boundaries of what's possible in tech.
My expertise spans across Machine Learning, Web Development, and DevOps, allowing me to tackle complex projects from multiple angles. I'm always eager to learn and apply new technologies to solve real-world problems.
Skills
Languages
Machine Learning & AI
Databases
Web Development
DevOps
Cloud & Code Management
Other Tools
Experience
AI Intern (Agent Architect)
Tech Profuse
May 2025 - Aug 2025
Developed, optimized and deployed real-timevoice agent for clients
Backend Developer
IdeaboxAI
Mar 2025 – July 2025
Work on the backend development team to upscale enterprise agents services
Student Intern
Incept AI
Feb 2025 – March 2025
Developed a system to address and fix the flaws in security copilot for the organization.
Software Development Intern
Binari Intelligence System Private Limited
Nov 2024 – Jan 2025
Designed a system to automate interview scheduling and skill assessments for candidates
ML Intern
Artsy Technologies
June 2024 – August 2024
Collaborated on machine learning models for creative applications and worked extensively on Reinforcemenrt Learning
Projects
Accessible AI - Sign Language Mapping
Engineered an innovative application for generating Indian Sign Language (ISL) videos from diverse media formats using Google Cloud Speech-to-Text for real-time transcription.
VerifyIt - Chrome Extension
Engineered a high-speed fact-checking Chrome extension using Flask, Google Custom Search API, Crawl4AI, and Gemini.
Deepfake Detection - Intel OneAPI Challenge
Designed and implemented a deepfake detection application using the ResNeXt-50 model, trained on the Celeb-DF (v2) dataset with 6,000+ samples, achieving 85% detection accuracy.
Femineh - Full stack Healthcare application
Developed a full-stack RAG application for personalized diet plan recommendations and food component analysis.
Automated User Recognition Application
Engineered and deployed a scalable AI attendance system for my department, utilizing a deep learning pipeline (MTCNNN FaceNet-SVM) on a Raspberry Pi for dual-layer authentication with RFID and facial recognition to eliminate proxy attendance. Built a React Native dashboard to manage logs and generate reports, currently under continuous development.
Godo - Opensource websocket load tester
Developed a A high-performance, native Go CLI tool for WebSocket load testing that leverages Go's efficient goroutines and channels for highly concurrent load generation. Library installation is available via go install github.com/SaiNivedh26/ws-load
