Hi There!

I'm Arindal Char

MERN <dev>, machine learning engineer

web3 & cloud enthusiast, tech geek

About Me

I am a final year Computer Science undergraduate at the University of Engineering and Management, Kolkata, deeply passionate about MERN development, Machine Learning and AWS.
My journey has equipped me with a strong foundation in these domains, allowing me to excel in both problem-solving, development and Machine Larning engineering. With hands-on experience in various projects, I strive to innovate and create impactful solutions in the ever-evolving tech landscape.

Skills

Experience

Education

Language
CPP, C, Java, JavaScript, Python, Shell, HTML/CSS
Frameworks
ReactJS, TailwindsCSS, Flask, Tensorflow, OpenCV, etc
Dev Tools
Git, Github, Linux
Others
AWS, APIs, MySQL

Coding
Gold Badges for C, CPP and Java in Hackerrank
Over 400 problems solved in Leetcode and CodeChef
Contests
Partcipated in multiple Coding Contests in Codechef, Leetcode, CodeForces
Certifications
AWS Fundamentals Specialization (Coursera)
AWS APAC's Solutions Architecture (Forage)
Machine Learning Speacialization (Coursera)

2021-2025
BTech - CSE (Artificial Intelligence & Machine Learning)
University of Engineering & Management, Kolkata
Current GPA: 8.33
2013-2021
Higher Secondary (ICSE)
St. Patrick's HS School, Asansol

My Projects

Blynk: RealTime Chat App

Blynk is a real-time messaging application built using the MERN stack. This project is designed to provide a platform for users to sign up, create their accounts, and engage in real-time text conversations with other users.

iPhone 13 Pro

iPhone 15 Pro landing page featuring dynamic animations powered by GSAP and an interactive 3D model of the iPhone created using Three.JS, and error tracking and performance monitoring through Sentry.

YOLOv5 Object Detection

A lightweight object detection model that uses YOLOv5's real-time object detection system and OpenCV libraries to detect and recognize a large number of stuff.

Threathunter ML

This project implements an Intrusion Detection System (IDS) for network security, utilizing a Random Forest model to detect anomalies in network traffic.