{
"name": "Pranjal Gupta",
"education": "CS @ Parul University ('27)",
"program": "Microsoft Industry Embedded Program",
"role": "Backend & Cloud Engineer",
"focus": [
"Building scalable backend systems",
"AI-powered applications (Azure OpenAI)",
"Real-world production architecture"
],
"certifications": [
"AZ-900", "AI-900", "PL-900", "PL-300",
"Oracle AI Vector Search"
],
"achievements": [
"Top 30% LeetCode Contests",
"Smart India Hackathon '25 Shortlist"
]
}
I believe in quality over quantity. My archived repos are my classroom; these are my workshop.
Java Spring Boot Azure OpenAI Microservices System Design
An AI-driven interview intelligence platform designed to bridge the gap between learning and landing the job.
- Core Idea: Simulates real-world technical interviews by providing concurrent, low-latency mock sessions with instant, AI-generated actionable feedback.
- Architecture: Backend-focused microservices architecture integrating Azure OpenAI (GPT-4o) and Azure AI Speech for real-time conversational data flow.
- Focus Areas: Low-latency concurrent processing, LLM prompt engineering, scalable data flow, and seamless cloud integration.
- Goal: Deliver an accessible, highly responsive, and intelligent interview preparation tool that scales efficiently under heavy user load.
Full-Stack React/TypeScript Node.js AI-Assisted
An end-to-end management platform empowering street vendors to plan inventory, track finances, and monitor market prices.
- Core Idea: Centralizes daily business operations by combining AI-assisted inventory recommendations with real-time market insights and financial tracking.
- Architecture: Full-stack monorepo featuring a React/Vite frontend and a modular Node.js/Express backend (MongoDB/Mongoose), complete with scheduled data ingestion pipelines for syncing external market APIs.
- Focus Areas: AI-driven inventory risk assessment, scalable service/controller API design, automated data scraping, and mobile-first data visualization.
- Goal: Transform scattered daily signals into actionable, data-driven insights so vendors can act quickly and maximize profitability.
🔹 Causa
GenAI Architecture
A failure diagnosis and observability platform designed to trace, analyze, and debug issues across distributed microservices.
- Core Idea: Centralizes logs, request flows, and service interactions using a unique request ID to reconstruct failure paths.
- Architecture: Backend system that aggregates logs from multiple services and correlates them into a unified trace.
- Focus Areas: Debugging distributed failures, request tracing, and improving system visibility.
- Goal: Reduce debugging time and bring clarity to complex microservice interactions.
I actively contribute to tools that solve real problems, focusing on API validation and documentation.
- Merged PRs in spring-blog-api
- Merged PRs in f1_info_API
- Contributions to ishpreet36752/Trawell