About Me
Engineering the Future
I am a dedicated Computer Science student with a deep passion for building software systems that bridge advanced intelligence with premium interface design. My studies and projects focus on creating production-grade applications that address real-world challenges.
Focused on AI/ML Integration, I specialize in architecting **Retrieval-Augmented Generation (RAG)** platforms, multi-agent AI networks orchestrated via LangGraph, and optimized edge systems like speech synthesis.
Through intensive learning experiences at **NxtWave** and academic projects, I have hardened my skills in TypeScript, React, Python, FastAPI, React Native, and robust database backends.
"Driven by clean code, interactive visual fidelity, and building local, privacy-first AI intelligence."
Academic & Experience Timeline
Computer Science Engineering
Noida / College Degree Study
Focused on Core Computer Science principles, Advanced Data Structures, Algorithms, Software Engineering, and AI/ML foundations.
Technical Specialization Study
NxtWave Intensive Program
Rigorous training in Full-Stack web architecture (MERN stack), React Native, mobile ecosystems, data scaling, and clean system design.
Open Source Contributor
GitHub Communities
Contributing to local multi-modal integrations, developer tooling, and building open-source community packages.
Tech Stack & Expertise
LangGraph
IntermediateRAG Systems
IntermediateOllama
Intermediateimport { LangGraph } from "@satyam/core";
// Skill Technical Spec Profile
export const spec = {
name: "LangGraph",
level: "Intermediate",
proficiency: 65%,
activeIn: ["Axion"]
};Featured Projects
IDE Case Studies
import fitz # PyMuPDF
from fastapi import FastAPI, UploadFile
from celery import Celery
from chromadb import VectorStore
app = FastAPI()
celery = Celery("rag_tasks", broker="redis://localhost:6379/0")
@celery.task
def process_pdf_pipeline(file_path: str):
# Asynchronous semantic chunking & embedding
doc = fitz.open(file_path)
chunks = []
for page in doc:
text = page.get_text("blocks")
chunks.extend(semantic_split(text))
embeddings = ollama.embed(model="nomic-embed-text", input=chunks)
vector_db.store(embeddings, metadata={"source": file_path})Transform static documents into conversational knowledge engines
The Problem
Static PDFs house vast amounts of proprietary data that remain inaccessible to simple search mechanisms. Organizations need a way to extract context-rich, semantic answers from multi-page PDFs securely without exposing data to external networks.
Technical Solution
Built an asynchronous pipeline leveraging FastAPI, PyMuPDF, and local embedding extraction. Uploaded documents trigger a Celery task that splits files by semantic boundary hooks. Vectors are indexed into ChromaDB. The conversational engine injects similarity chunks directly into the LLM context window, returning answers complete with page reference tags.
Key Challenges & Solves
Handling massive PDF documents with multi-column layouts, tables, and complex headers/footers without mixing context.
Managing processing latency for chunk parsing and vector calculations on background threads.
Providing references to exact source pages to eliminate LLM hallucinations.
System Topology
GitHub Contributions
Contribution Heatmap (2025/2026)
23 Public RepositoriesKey Commit & PR Logs
EventOne platform check-in logic
Optimized Socket.IO connections to support 500+ concurrent live check-ins.
Attendance mobile Telegram OAuth link
Developed custom authentication tokens issued by a Telegram bot to handle user dashboard logins.
LangGraph local Ollama routing
Implemented tool call fallbacks when local model inference output is poorly formatted.
Experience & Certificates
Work & Community
GSSoC '26 Contributor
GirlScript Summer of Code • Part-time
Contributing to open-source projects under the specialized AI Agents track. Developing and deploying intelligent automated agents and systems.
Member
Internet Society • Community member
Engaging in activities focused on internet governance, open standards, policy development, and digital inclusivity.
Outreach Head
Advanced Tech Club, NIAT Delhi • Leadership role
Leading tech outreach initiatives, organizing technical events, workshops, and fostering community collaborations around emerging technologies.
Verified Credentials
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Get In Touch
Let's build something extraordinary
I am open to internships, open-source projects, or technical collaborations. If you have an idea, a question, or just want to chat, fill out the form or reach out directly on social media.