Portfolio
Available for opportunities

Hey, I'm Aman Kushwaha

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Results-driven Data Science undergraduate with hands-on experience in machine learning, AI systems, full-stack development, and open-source projects. Passionate about building scalable solutions, learning emerging technologies, and collaborating with diverse teams to solve complex challenges. Seeking opportunities to contribute technical expertise while continuing to grow as an engineer.

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Aman Kushwaha
⚡ Next.js 16
🤖 AI/ML
10+
Projects Built
20+
Technologies
50+
GitHub Stars
200+
Contributions

Terminal

bash — amank-root@portfolio
Welcome to Aman's Portfolio Terminal v2.0.0 — Type 'help' for commands

Recent Projects

Redesigned Better Auth and Razorpay integration cover showing checkout, billing, and webhook capabilities
completed

Better Auth Razorpay Plugin – Payments, Subscriptions & Webhooks

A comprehensive Razorpay integration plugin for Better Auth, bringing one-time payments, recurring subscriptions, invoices, refunds, customer management, marketplace transfers, and webhook handling into a unified, type-safe TypeScript API.

TypeScriptBetter AuthRazorpayNode.js+6
VoxRAG voice-first retrieval augmented generation banner with microphone, audio waveform and knowledge documents
completed

VoxRAG

VoxRAG is a voice- and text-enabled Retrieval-Augmented Generation system built on MSMARCO-XI. Speak or type in English, हिन्दी, मराठी, বাংলা, മലയാളം, ગુજરાતી, অসমীয়া — the pipeline transcribes, retrieves grounded passages from ~3.2M (32 lakh) embedded records, and streams a cited answer back, targeting <200 ms retrieval / time-to-first-token.

PythonStreamlitSarvamAIGroq+5
IITD_Feb26_AAIPL
completed

AAIPL Tournament: Dual-Agent Fine-Tuning Solution

Won the AAIPL Hackathon (1st place out of 120 teams) by building a dual-agent LLM system using Qwen2.5-14B-Instruct. Developed end-to-end fine-tuning pipelines with LoRA and 4-bit quantization to train a Question Agent for generating MCQs and an Answer Agent for solving them with structured reasoning. Implemented robust data cleaning, custom dataset formatting, and efficient training workflows for high performance under constrained compute.

Python Jupyter NotebookUnSlothLoRa
AkashSetu exoplanet discovery using NASA datasets, illustrated with an exoplanet and transit signal
completed

AkashSetu

AI-enabled detection of exoplanets from noisy TESS light curves, using a Box-Least-Squares transit search feeding a calibrated XGBoost + Random-Forest ensemble, plus an LLM (via Groq) that explains and discusses each result with you.

Python Jupyter NotebookXGBoostGroq