👋 Hello, I'm

Apoorv Maurya

AI/ML Engineer · Multi-Agent Systems & LLM-Integrated Products

Building innovative solutions at the intersection of full-stack development and artificial intelligence. Specializing in autonomous multi-agent frameworks, LLM integrations, and robust real-time applications.

About Me

AI/ML Engineer | Multi-Agent Systems & LLM-Integrated Products

I'm an AI/ML Engineer and Full-Stack Developer with a passion for creating innovative solutions that combine cutting-edge web technologies with artificial intelligence. I specialize in building autonomous multi-agent systems and LLM-integrated products that deliver exceptional, intelligent user experiences.

My expertise spans across multi-agent orchestration frameworks (LangChain, LangGraph), LLM APIs (OpenAI, Claude, Groq Llama), modern web frameworks (Next.js, React), and scalable backend architectures (FastAPI, Node.js). I'm constantly exploring new technologies and patterns (like Human-in-the-Loop checkpoints and pgvector RAG-style AI layers) to stay at the leading edge.

Beyond core engineering, I'm committed to giving back to the developer community through open-source contributions, mentoring student chapters, and organizing tech events. I believe in fostering collaborative learning and bridging students directly with industry leadership.

1-2

Years Experience

10+

Projects Completed

100%

Client Satisfaction

Work Experience

Full-Stack Developer Intern

DevX Days

January 2025 - September 2025

▹Architected async data ingestion pipelines in Next.js + TypeScript, directly applicable to the queue-based OCR and document ingestion patterns (SQS/BullMQ) required for legal document workflows.
▹Built API-response caching layers (SWR) reducing redundant calls by ~60% per session.
▹Implemented streaming response delivery and lazy-loading patterns that cut initial load time by 40% - directly transferable to streaming LLM draft generation required for real-time legal document creation.
▹Engineered multi-step validated UI flows with mandatory confirmation gates for critical user actions which directly mirrors the Human-in-the-Loop checkpoint design required in agent architectures.

Next.jsTypeScriptSWRBullMQSQS

Frontend Developer Intern

Pixelwand

July 2024 - December 2024

▹Converted complex Figma designs into pixel-accurate React components using styled-components, validated pixel accuracy across the mobile, tablet, and desktop breakpoints via component-level QA.
▹Designed and documented a reusable React component library that measurably reduced the feature development time by ~25%, establishing the team's shared UI standard across all client products.

Reactstyled-componentsFigma

Technical Skills

Languages

TypeScript90%

JavaScript95%

Python85%

SQL80%

Frontend

React.js95%

Next.js (App Router / SSR)95%

Tailwind CSS90%

Backend & Database

Node.js90%

Express.js85%

FastAPI85%

LangChain / LangGraph85%

Claude / OpenAI / Groq85%

Supabase90%

MySQL80%

AI & ML

Vercel AI SDK85%

LLM Integration90%

scikit-learn80%

Tools & Platforms

Git95%

Docker80%

AWS Textract75%

S3 / MinIO80%

BullMQ / SQS80%

Postman90%

Vercel90%

Render80%

Figma75%

Featured Projects

RetentIQ

Multi-agent churn-intelligence SaaS platform combining scikit-learn and LangChain-orchestrated LLMs.

FastAPIPythonscikit-learn+5

Planora

Real-time collaborative trip planning platform with a pgvector RAG-style AI layer.

Next.jsTypeScriptSupabase+3

Data Alchemist

AI Resource Allocation Configurator

Next.jsTypeScriptGoogle Gemini+1

AssessMed

Clinical decision interface exposing FastAPI-served ML prediction models.

Next.jsTypeScriptPython+3

AI Companion

Video Call & Streaming Platform

Next.jsTypeScriptTailwindCSS+4

iCloud Clone

Cloud storage solution

ReactNode.jsAWS S3+1

Reusable AI Engineering Showcase

AI Engineering Lab

Curated Agentic UX design patterns and clean mathematical algorithms optimized for real-time production AI pipelines.

Token Streaming Simulation

Active Stream Ready

Simulates chunk-based text streaming generated via Server-Sent Events (SSE) direct to the UI.

Click the button below to initialize the LLM generation pipeline...

useLLMStream.ts

1// Reusable React Hook for SSE LLM Token Streaming

2import { useState, useCallback } from 'react';

4export function useLLMStream() {

5 const [data, setData] = useState('');

6 const [isStreaming, setIsStreaming] = useState(false);

8 const stream = useCallback(async (url: string, body: any) => {

9 setIsStreaming(true);

10 setData('');

11 try {

12 const response = await fetch(url, {

13 method: 'POST',

14 headers: { 'Content-Type': 'application/json' },

15 body: JSON.stringify(body)

16 });

18 const reader = response.body?.getReader();

19 const decoder = new TextDecoder();

20 if (!reader) return;

22 while (true) {

23 const { value, done } = await reader.read();

24 if (done) break;

25 const chunk = decoder.decode(value, { stream: true });

26 setData(prev => prev + chunk);

27 }

28 } catch (err) {

29 console.error('LLM stream error:', err);

30 } finally {

31 setIsStreaming(false);

32 }

33 }, []);

35 return { data, isStreaming, stream };

36}

Community Involvement

💻

Open Source Contributions

Active contributor to various open-source projects on GitHub

2023-Present

🎤

Workshops

Conducted workshops on APIs, Postman, and more

2023-2024

👨‍🏫

Volunteering & Co-organizer Events

Volunteered and co-organized events for the OpenSourceDays community

2022-2025

Get In Touch

Have a project in mind or want to discuss opportunities? Feel free to reach out! I'm always open to interesting conversations and collaboration.

Email

apoorvmauryaapoorv@gmail.com

Location

India

Response Time

Within 24 hours