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Full Stack Developer · Applied AI
Full-time · May 2024 – Present@ M37 Labs

Building production AI systems for enterprise clients across India and Malaysia. Multi-agent platforms, NL-to-SQL over 200K+ publications, AI recruitment pipelines — 99.9% uptime, 1000+ daily automated workflows.

Full Stack Intern
@ Luchkee Health · Dec 2024 – Feb 2025

MERN stack healthcare platform. MongoDB query optimisation cut dashboard load from 3s → under 1s. API validation reduced invalid requests by 80%.

Core AI & Stack Variables
RAG PipelinesMulti-Agent SystemsLoRA Fine-tuningNL-to-SQLMCP ServersLangChainFastAPINext.jsPostgreSQLDockerRedisQdrant
Certifications
Data Analytics with Python (NPTEL — 2025)
Introduction to Database Systems (NPTEL — 2025)
GitHub Profile
# [about] exit 0

./experience -v

A log of professional history and built systems

history_log[0].json

Full Stack Developer · Applied AI

M37 Labs
May 2024 – Present
Delhi, India
Full-time
// Task execution log
  • ->Built and deployed several AI features for clients across India and Malaysia.
  • ->Created a custom workflow platform with Next.js and FastAPI that acts like a smart router, sending over 1,000 tasks daily to different specialized AI agents.
  • ->Added a natural language search to a 200,000+ publication database using LangChain. I used Redis heavily here to keep the query times down.
  • ->Automated resume screening using spaCy for text parsing and GPT-4 for scoring candidates.
  • ->Trained a scikit-learn email classifier to categorize incoming messages and draft automated replies.
  • ->Shifted a lot of our heavy API data aggregation to PostgreSQL and added Redis caching, which noticeably improved response times.
  • ->Set up deployments across AWS, Vercel, and Render using Docker.
history_log[1].json

Full Stack Developer Intern

Luchkee Health Pvt. Ltd.
Dec 2024 – Feb 2025
Delhi, India
Internship
// Task execution log
  • ->Worked as a full stack developer on a healthcare platform using the MERN stack.
  • ->Wrote Zod schemas for all our APIs to catch bad requests early.
  • ->Added indexes to MongoDB and profiled slow queries to drop the main dashboard load time from 3 seconds to under 1 second.
# [experience] exit 0

prince@macbook:~/skills$pkg list --installed --icons

System capabilities and installed technical packages

Frontend
React
React
Next.js
Next.js
TypeScript
TypeScript
JavaScript
JavaScript
Tailwind CSS
Tailwind CSS
Framer Motion
Framer Motion
Backend
Python
Python
FastAPI
FastAPI
Node.js
Node.js
Express.js
Express.js
Flask
Flask
Streamlit
Streamlit
AI / LLM
OpenAI GPT-4
OpenAI GPT-4
Anthropic Claude
Anthropic Claude
Google Gemini
Google Gemini
LangChain
LangChain
L
LoRA Fine-tuning
R
RAG Pipelines
M
Multi-Agent Systems
M
MCP Servers
s
spaCy
M
MediaPipe
Databases
PostgreSQL
PostgreSQL
MongoDB
MongoDB
Redis
Redis
MySQL
MySQL
SQLite
SQLite
p
pgvector
C
ChromaDB
Q
Qdrant
Infrastructure
Docker
Docker
AWS EC2
AWS EC2
Vercel
Vercel
Render
Render
Celery
Celery
Socket.IO
Socket.IO
OAuth 2.0
OAuth 2.0
JWT
JWT
ML / Data
NumPy
NumPy
Pandas
Pandas
Scikit-learn
Scikit-learn
OpenCV
OpenCV
M
MLX-LM
TensorFlow
TensorFlow
PyTorch
PyTorch
# [skills] exit 0

./projects -l

Active repository list and deployed environments

Flags:
mental_health_ai.exe
// mental_health_ai
Fine-tuned · 14 classes · Apple Silicon
ML Research

Mental Health AI

Fine-tuned a Qwen-3B model entirely on Apple Silicon just to see how far I could push local hardware. I scraped 65,000 Reddit posts, cleaned the dataset, and generated synthetic replies using Ollama. It serves as both a 14-class symptom classifier and an empathetic chatbot. Served via FastAPI.

PythonMLX-LMLoRAQwen2.5FastAPIOllama
multi-agent_workflow_platform.exe
// multi-agent_workflow_platform
Production · 1000+ daily workflows
AI Systems

Multi-Agent Workflow Platform

Our production platform for running different specialized AI agents. You type a prompt, and a routing engine decides which agent should handle it (drafting emails, managing calendars, processing docs). It runs hundreds of automated tasks daily.

PythonFastAPILiteLLMOpenAIMCPRedisNext.js
Private
nl-to-sql_media_monitoring_chatbot.exe
// nl-to-sql_media_monitoring_chatbot
200K+ publications · 5s → 2s
AI Systems

NL-to-SQL Media Monitoring Chatbot

A chat interface connected to a 3.6 GB MySQL database of publications. Users ask questions in plain English, and LangChain writes the SQL to fetch the answers. I added Redis caching so frequent queries load instantly.

PythonFastAPILangChainMySQLRedisGPT-4
Private
ai_recruitment_screening_system.exe
// ai_recruitment_screening_system
95%+ accuracy · 70% time reduction
AI Systems

AI Recruitment Screening System

An internal tool for HR to speed up hiring. It uses spaCy to extract context from a resume rather than just matching keywords, and then GPT-4 scores the candidate against the job description. It processes hundreds of resumes in seconds.

PythonFastAPIspaCyOpenAIScikit-learnPDF parsing
Private
delhi_pm2.5_aqi_forecasting.exe
// delhi_pm2.5_aqi_forecasting
R²=0.9501 · Built from scratch
ML Research

Delhi PM2.5 AQI Forecasting

My final year university project. I wanted to understand the math behind backpropagation, so I built a neural network completely from scratch in pure NumPy (no PyTorch or TensorFlow) to predict Delhi's air quality using 6 years of sensor data.

PythonNumPyPandasScikit-learnCPCB Data
intelligent_proctoring_framework.exe
// intelligent_proctoring_framework
95%+ detection accuracy
AI Systems

Intelligent Proctoring Framework

A real-time browser-based exam monitoring tool. It tracks where the user is looking and checks for multiple faces or objects to detect cheating. Mostly an excuse for me to play around with face-api.js and MediaPipe.

PythonOpenCVMediaPipeFlaskface-api.js
# [projects] exit 0

./connect_agent.sh

Initialize secure connection protocol

network_visualizer.exe
[OK] Active globally
Listening
Delhi, India → Node: Remote Target
secure_payload_sender.sh
prince@macbook:~ $ echo "© 2026 Prince"Built with Next.js · TypeScript · Tailwind · Framer Motion