Madhav Jangid Projects — AI Applications and Full Stack Portfolio
Explore projects by Madhav Jangid, a software engineer specializing in AI-powered platforms, developer tools, and scalable full stack applications used by thousands of users worldwide.
My Work
AI-powered platforms and full stack projects
built to scale
Personal Projects
Let's begin with the projects I built while learning, crafted as if they were meant to power the next big thing.
PROFESSIONAL WORK
Some projects come with an NDA - basically "cool story bro... but don’t tell anyone."
FastAPI Inference Engine
I built a high-performance inference pipeline that served three custom ML models at once. Added async processing so it could handle multiple facial analysis and diagnostic requests in parallel — smooth, fast, and production-ready.
Team Structure & Speed
Took charge of a 5-member team. Split the entire codebase into clean, independent modules. Merge conflicts disappeared and our development speed literally doubled.
Smart Recommendation System
Designed and shipped a scalable recommendation engine that turned raw data into personalized, intelligent insights for users.
Live on AWS
Deployed everything on AWS ECS + ECR with an Application Load Balancer so the service stayed fast and available under real traffic.
The X Factor
Don't Underestimate the power of Todo App
The whole logic of this todo app is written with BroCode, Yeah you heard it right my own BroCode
Try the language yourself or support the project.
Development Process
From Idea to Production.
Problem & Requirements
Every system begins with understanding user needs, technical constraints, and defining clear success metrics before development starts.
Architecture & Design
I design scalable system architectures, APIs, and data models focused on performance, maintainability, and long-term growth.
Build & Iterate
Features are developed incrementally with continuous testing, feedback loops, and rapid iteration to ensure stability.
Deployment & Optimization
Applications are deployed with monitoring, optimization, and scalability in mind to support real-world production workloads.
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