Currently leading frontend for an enterprise AI platform

AI products win or loseat the interface.I build the one that wins.

I'm Jeet — a lead frontend engineer who owns the UI layer of AI and SaaS products end-to-end. Real-time, LLM-native interfaces on React, Next.js, and TypeScript. From architecture to production, shipped fast.

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shipping in production
9 yrs

shipping in production

concurrent users served
100k+

concurrent users served

in production today
Enterprise AI

in production today

Interfaces I've architected and shipped into production

ArchipelagoFanblastStreamblastNFL AnalyticsImmozyWhow GamesScrubPoolReelo

The problem

A great model isn't a great product.

Users never experience your weights — they experience the interface. That's where trust, adoption, and churn are decided, and it's where most teams are thinnest. Bring me in to own exactly that layer.

What I own

AI product interfaces

Make the model feel trustworthy.

Streaming responses, confidence signals, graceful fallbacks, and human-in-the-loop flows. I turn raw LLM output into interfaces non-technical users actually rely on.

  • Streaming & token-level UI
  • Confidence & citation states
  • Agentic / review workflows

Frontend architecture

Own the UI layer end-to-end.

Design systems, real-time data-rich dashboards, and a component architecture that stays fast and maintainable as the product and team grow past the demo.

  • Design systems & tokens
  • Real-time, data-heavy UIs
  • Performance at scale

Shipping velocity

Move fast without the debt.

Senior judgment on what to build and what to skip. I go from architecture to production quickly, set the quality bar, and lift the engineers around me.

  • Architecture to production
  • CI/CD & quality standards
  • Mentorship & code review
Not sure which of these you need?Tell me what you're building

Selected work

Shipped, at scale, under real load.

FlagshipLead Frontend Engineer · Present

Archipelago

Enterprise AI platform for commercial-property insurance

I own the frontend architecture for an AI platform where brokers make high-stakes decisions on real money. I built the company-wide design system, the real-time data-rich UI, and the LLM output layer — streaming, confidence indicators, and graceful fallbacks that make AI insights trustworthy under production load.

ReactNext.jsTypeScriptGraphQLGoodData

What I own

  • Company-wide design system
  • LLM output UI in production
  • Real-time AI insights at scale
Sr. Frontend Engineer

Fanblast

Live event SaaS · US & Germany

Led the frontend of a live-event platform handling 50k+ concurrent users. Connected 2k+ creators to millions of fans and shipped real-phone-number SMS chat plus live ad panels that lifted engagement and ad revenue.

  • 50k+ concurrent
  • 2k+ creators
  • Live SMS + ads
ReactNext.jsTypeScriptFirebase
Sr. Frontend Engineer

Streamblast

Live-show streaming · Digital Blast GmbH

Key frontend engineer on a live-show platform that ran "Show Your Talent" at 100k concurrent viewers, plus 48h+ marathon events with German celebrities — real-time voting and interaction that held up under spikes.

  • 100k concurrent
  • 48h+ live events
  • Real-time voting
ReactTypeScriptFirebase
Sr. Frontend Engineer

NFL Analytics

Sports performance analytics

Built the interface for an NFL analytics tool using Amazon Rekognition and Textract to turn game footage into player-performance insight that shaped team strategy.

  • Vision + OCR pipeline UI
  • Data-driven analysis
ReactReduxTypeScriptAWS
Also shippedImmozy · real estateCasino games · PIXI.jsScrubPool · healthcare hiringReelo · loyalty

How we'd work

Low friction. High signal.

Working with a senior engineer shouldn't feel like a gamble. Here's exactly how a collaboration goes from first message to shipped.

  1. 01

    A short intro call

    We talk through what you’re building and where the real frontend or AI risk sits. No pitch — just an honest read on whether I can move the needle.

  2. 02

    Scope & architecture

    I map the shortest path to a production-quality result, define where I own versus collaborate, and flag the decisions that matter before a line of code.

  3. 03

    Ship in tight loops

    Working software fast, in reviewable increments. You see real progress every week, not a big reveal at the end — so we can course-correct early.

  4. 04

    Own the outcome

    Performance, the quality bar, and the engineers around it. I leave the codebase — and the team — in a better place than I found them.

Track record

Nine years, two companies, one throughline.

Depth over churn. I stay long enough to own outcomes, not just tickets.

Lead Frontend Engineer

Archipelago Analytics

Oct 2024 — Present · Remote

Own end-to-end frontend architecture for an enterprise AI platform in commercial-property insurance — design system, real-time data-rich UI, and the LLM output layer. Set technical direction and mentor the frontend team.

Senior Frontend Engineer

Scaletech Solutions

Apr 2017 — Oct 2024 · Ahmedabad, India

Seven years building and scaling web products across SaaS, live-streaming, sports, real estate, and gaming. Shipped reusable component libraries, CI/CD pipelines, and accessible interfaces used by hundreds of thousands of users — growing into a senior technical lead.

The stack

Deep where it counts, fluent everywhere else.

Core

ReactNext.jsTypeScriptJavaScript

AI integration

LLM APIsStreaming UIsRAGAgentic workflowsLangChain

Frontend craft

Design systemsGraphQLReduxTailwindWeb performanceWCAG a11y

Platform

Node.jsAWSVercelCI/CDDockerGitHub Actions

Quality

JestCypressReact Testing LibraryCode review standards

Start here

Is the interface where your product lives or dies?

If you're a founder or team shipping AI or SaaS and the frontend is where your risk sits, tell me what you're building. I take on a small number of collaborations at a time — the ones with a real engineering challenge worth solving.

Start a conversationjeetrabadiya@gmail.com

Usually replies within a day or two.