Available for work

Building for themodern web.

Full Stack DeveloperAI/ML EngineerInterface Craftsman

I'm Aman Kushwaha. I design and build fast, accessible web products — and the machine learning systems that power them.

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Résumé

Aman Kushwaha

New Delhi

OPEN

Projects shipped

10+

Commits this year

200+

Projects shipped

Technologies

GitHub stars

Commits this year

What I do

Four things I spend most of my time on.

Not a services list — these are the problems I keep asking for.

Product engineering

Full-stack Next.js and TypeScript — design systems, data layers, and the unglamorous parts that keep things fast and accessible.

Applied machine learning

Retrieval pipelines, fine-tuning and evaluation, wired into real products instead of left in notebooks.

Interface craft

Typography, spacing, motion and accessibility — the difference between a page that works and one people enjoy.

Platform work

CI, content pipelines, analytics and docs: the infrastructure that keeps a team shipping without drama.

Selected workAll projects

Things I shipped recently.

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.

Aug 2026View case

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.

Feb 2026View case

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.

Jul 2026View case

completed

Genome AI: Cattle Breed Classification from SNP Data

A reproducible machine learning pipeline for cattle breed classification from SNP genotype data, featuring CNN and Transformer baselines. Includes data validation, preprocessing, training, and benchmarking workflows to compare local vs long-range genomic pattern modeling.

Jan 2026View case

StackFull list

Tools I reach for.

Node.js Express.jsREST APIsJWTAuth.jsRedisGit GitHubVersion ControlOpen SourceTailwind CSS Material UIResponsive DesignCSS3VS Code npm PostmanChrome DevToolsReact.jsNext.jsReduxReact HooksProblem Solving Team CollaborationTime ManagementCommunicationVercel NetlifyCloudflare Pages Cloudflare WorkersMongoDB Mongoose Database DesignCRUD OperationsMySqlNeonDBScikit-learnPytorchTensorflowUnslothReinforcement LearningLoRaQLoRaJavaScript ES6+ HTML5 CSS3 TypeScriptNode.js Express.jsREST APIsJWTAuth.jsRedisGit GitHubVersion ControlOpen SourceTailwind CSS Material UIResponsive DesignCSS3VS Code npm PostmanChrome DevToolsReact.jsNext.jsReduxReact HooksProblem Solving Team CollaborationTime ManagementCommunicationVercel NetlifyCloudflare Pages Cloudflare WorkersMongoDB Mongoose Database DesignCRUD OperationsMySqlNeonDBScikit-learnPytorchTensorflowUnslothReinforcement LearningLoRaQLoRaJavaScript ES6+ HTML5 CSS3 TypeScript

VoxRAGAAIPL Tournament: Dual-Agent Fine-Tuning SolutionAkashSetu