Achintya Rai
Data Scientist & Backend Engineer
Achintya Rai

Building intelligent systems that matter.

I am a Data Scientist and AI Researcher passionate about bridging the gap between raw data and actionable intelligence. As a 4th-year CSE student, I specialize in crafting robust RAG pipelines, autonomous agents, and evaluating internal reasoning in large language models. Beyond building scalable backend platforms, I thrive on solving complex algorithmic challenges and pushing the bleeding edge of NLP research.

Featured Work

TransitOps

An enterprise-grade smart transport operations platform with atomic ACID state machines, multi-persona RBAC, and real-time fleet analytics.

Architecture: React (Vite) frontend with Express backend, PostgreSQL 18, and Prisma ORM utilizing atomic database transactions.
Explanation: Eliminates dispatch race conditions and compliance risks with database-enforced state machines and a 4-tier role-based access control matrix.
React Node.js PostgreSQL Prisma ORM

NOMADIX

An all-in-one collaborative travel portal with democratic AI destination recommendations, interactive itineraries, real-time chat, and expense splitting.

Architecture: React 19 & Vite frontend with Node.js/Express 5 backend, MongoDB, and Socket.io for real-time WebSocket synchronization.
Explanation: Eliminates friction in group trips by using statistical consensus algorithms for destination voting, live collaborative itinerary builders, and automated group treasury accounting.
React 19 Node.js Socket.io MongoDB

STRATEGOS

A full-stack autonomous data analyst agent. It ingests datasets, reasons via a ReAct loop, and executes Python code in a secure sandbox.

Architecture: React frontend with FastAPI and Express Node.js (MongoDB) microservices.
Explanation: Acts as an autonomous data scientist. It processes raw data, understands user queries, and dynamically writes/executes code to generate insights and visualizations.
React FastAPI Node.js MongoDB

JurisAI

A specialized Legal AI chatbot designed for the Indian judicial system. It uses RAG pipelines to retrieve case laws and legal precedents.

Architecture: Python backend utilizing pgvector for vector storage and semantic search, integrated with LLMs for natural language generation.
Explanation: Empowers legal professionals by parsing complex queries and fetching highly relevant case laws from a specialized vector database, providing accurate and context-aware legal assistance.
Python pgvector LLMs NLP

Swiggy MERN Platform

An authentic, full-stack food delivery platform with zero-config in-memory database auto-seeding, live order tracking, and dynamic promo validation.

Architecture: React 19 & Vite frontend with Express/Node.js REST API, MongoDB, and dual-mode client fallback engine for static hosting.
Explanation: Delivers gourmet food discovery and multi-step order timelines both locally connected to MongoDB and statically hosted right in the browser on GitHub Pages.
React 19 Node.js MongoDB Express

Tech Stack

AI & Data Science

Building intelligent systems, autonomous agents, and RAG pipelines. Specialized in transforming raw data into actionable intelligence and fine-tuning language models.

Python LLMs & NLP RAG Pipelines pgvector

Backend Engineering

Architecting scalable APIs, secure microservices, and robust databases.

FastAPI Node.js Express PostgreSQL MongoDB

Frontend

Responsive user interfaces.

React

DevOps

Containerization & CI/CD.

DockerGit
  • PostgreSQL
  • Python
  • React
  • Node.js
  • Docker

Beyond the Code

Currently Listening To
Click to Play
Slow Dancing in a Burning Room
John Mayer
Currently Reading
Meditations
Marcus Aurelius
Currently Exploring
Novel NLP Evaluation Metrics

Let's build together.

I'm currently open for software engineering internships and NLP research collaborations.

achintya.code@gmail.com Telegram Discord