Atharva Tayade

Built toRun alone

Job pipeline

Typical daily run

0listings
0leads
  • 7f2abackend engineergreenhouse
  • 7f2bbackend engineerlinkedin
  • 7f2cfull stack developerlever
  • 7f2dsr. backend engineeraggregator
  • 7f2eml engineercareers

Three-layer Redis dedupe. The same role posted to a board, an aggregator, and a recruiter feed counts once.

I'm Atharva Tayade, 22.
I build systems that keep running when nobody is watching them. Pipelines, scrapers, and scoring models, plus the web platforms they sit behind.

Bachelor of Engineering (B.E.), Fr. C. Rodrigues Institute of Technology (FCRIT) · University of Mumbai

POWER (Instrumental)

KANYE WEST

LOADING...

Approach

I like the problems that only appear at scale. The duplicate that arrives under a different name. The source that changes its markup overnight. The credit score nobody can defend to a regulator. Most of what I build is meant to survive contact with those while I am asleep, which means the interesting work is in the failure paths, not the happy one.

SelectedWork

Six things I built and can still explain. Three have a screen; three are pipelines, so they show their stages instead.

9 projects
VRI CRED interface
2026

VRI CRED

MSME credit scoring for NBFC underwriters, with an explanation attached to every score.

FastAPIXGBoost
Axiom Designs interface
2025

Axiom Designs

Freelance web design and development studio.

FreelanceReact
REFERENCE HUB interface
2025

REFERENCE HUB

500+ developer reference entries, searchable, related by a D3.js graph, and never manually updated.

ReactD3.js

Strokes Designs

Creative agency portfolio and services platform.

FreelanceUI/UX

JOB PIPELINE

A distributed scraper across 50+ job portals that turns 1,000+ raw listings into 500-750+ deduplicated leads a day.

ScrapyPlaywright

SCRAPING ENGINE

A site-adaptive crawler holding about a 95% success rate across mixed, hostile sources.

ScrapyPlaywright

OSINT AGGREGATOR

DNS, WHOIS, and public-record aggregation with a PyTorch classifier labelling signals at 89% accuracy.

ScrapyPyTorch

IPL PREDICTION

A gradient boosting model on match data, built as a study in calibration rather than a chase for accuracy.

scikit-learnXGBoost

Services

WEB DEVELOPMENT

UX/UI DESIGN

APP DEVELOPMENT

BRAND DESIGN

AUTOMATE

THE

PART

YOU

KEEP

DOING

TWICE

Stack

What I actually reach for, and the work it earned its place on. Nothing is listed here that is not behind something else on this site.

Python
LanguagePipelines, models
TypeScript
LanguageClient platforms
JavaScript
LanguageClient platforms
SQL
LanguageEverywhere
React
Frontend6 shipped platforms
Next.js
FrontendClient platforms
Tailwind CSS
FrontendDesign systems
D3.js
FrontendReference hub
FastAPI
BackendVRI Cred, OSINT
Node.js
BackendClient backends
.NET Core
BackendSumati.io
PostgreSQL
DatabaseEvery pipeline
Redis
DatabaseDedupe, caching
MongoDB
DatabaseClient platforms
Scrapy
DataCrawlers
Playwright
DataJS-rendered sources
pandas
DataFeature work
XGBoost
MLVRI Cred, IPL
PyTorch
MLOSINT classifier
SHAP
MLScore explanations
Claude API
MLClassification, drafting
Docker
InfraPipelines
AWS
InfraEC2, RDS, S3
Linux
InfraEverywhere
Git
InfraEverywhere
Vercel
InfraThis site

Experience

Freelance since 2023, plus a software internship. Numbers below are the ones I measured, not estimates.

Freelance

2023 — Present · Mumbai

Full-Stack Developer

  • Built and shipped 6 web platforms (e-commerce, SaaS, client portals) on React/Next.js frontends with FastAPI and Node.js behind them
  • Set up Stripe subscription billing, JWT authentication, and role-based access control across 3 client projects
  • Integrated ML inference endpoints into 2 client products

Sumati.io

Jan — Mar 2024 · Remote

Software Engineering Intern

  • Rebuilt the policy module UI in React with component-level code splitting; page load time dropped 45%, measured in Lighthouse
  • Added response caching to .NET Core API endpoints, cutting redundant backend calls about 60% on the highest-traffic policy routes
  • Built a monitoring dashboard tracking request latency, error rates, and service health across 4 microservices

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