Subhajit Gorai

IOS Engineer

Kolkata, West Bengal, India0 mo experience

Key Highlights

Developed AI-driven health app for families.
Created a Java IDE enabling on-device compilation.
Built ML system predicting student performance with high accuracy.

Stackforce AI infers this person is a Mobile Application Developer with expertise in AI and Machine Learning in the Healthcare and EdTech sectors.

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Skills

Core Skills

Mobile Application DevelopmentAndroid DevelopmentMachine Learning

Other Skills

Android StudioKotlinJavaFirebaseEclipse JDT CoreRandom ForestPythonReact NativeXMLMicroPythonHTML5XHTMLCascading Style Sheets (CSS)SCSSJavaScript

About

I've been passionate about tech since the late 2016. I started building websites with PHP since then and used free domains from Freenom.com paired with Cloudflare CDN to host them on shared hosting sites. Although, I've moved on to different tech stacks now, the experience that I've brought from the past by building stuff, remains consistent.

Experience

0 mo

Total Experience

Average Tenure

Current Experience

Super.family

SDE Intern

Aug 2025 – Present · 9 mos · Hyderabad · Remote

Working on a family-focused AI health & wellness app-based start-up where I'm implementing the core tracking workflows and the family connectivity features of the Android & iOS app.
Addressed performance issues and design flaws in the MVP and worked on rebuilding the full app from scratch.

Android StudioMobile Application DevelopmentKotlinJavaFirebaseAndroid Development

Euphoria genx

Android Developer Intern

Jun 2025 – Aug 2025 · 2 mos · Kolkata, West Bengal, India · Hybrid

Worked on a professional Java IDE for Android with Eclipse JDT Core compiler integration, enabling on-device Java compilation and bytecode execution on Android devices.

JavaEclipse JDT CoreAndroid Development

Edunet foundation

AI Intern

Jun 2025 – Jul 2025 · 1 mo · Bengaluru, Karnataka, India · Remote

An internship initiated by Microsoft in collaboration with Edunet Foundation where I worked on a comprehensive machine learning system that predicts student academic performance with 79% accuracy using Random Forest algorithms, achieving 80-90% accuracy with advanced ML pipelines.

Machine LearningRandom ForestPython