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Open to AI infra & backend roles

I build the backend & infrastructure that AI runs on.

I'm Sarthak Agrawal. I spend my time on the backend — distributed systems, fast APIs, data pipelines, and the infrastructure that keeps AI features dependable once real traffic shows up.

View selected work About me
sarthak@infra — zsh — 80×24
4+
years shipping
200k
daily actives served
116
public repositories
6
languages in prod

// how i think

It's the stuff around the happy path.

Most of the job isn't the code that works first try. It's the request that times out, the queue that backs up, the dependency that goes down. That's what I build for.

~/architecture — request lifecycle
Clients web · mobile Edge / CDN cloudflare API Gateway auth · routing Kafka event stream Vector Search milvus · embeddings MySQL primary store Workers go services Redis cache · realtime
ingress compute stateful

// selected work

Work, written up properly.

Four projects from my day jobs — what the problem was, what I built, and what actually changed.

01 2024

Vector-Powered Personalized Feeds

embeddings · vector search · ranking

A home feed that actually learns what you like. Content gets embedded with BERT, ranked by similarity in Milvus, and the user vector keeps updating from live events. Engagement went up 40%.

+40%
home-feed engagement
Real-time
user vectors
Milvus
ANN vector search
Go Milvus BERT OpenAI / GPT BigQuery read case study →
02 2023

Real-Time Market Data Pipeline

streaming · protobuf · fan-out

The streaming backbone of a fintech social app. Go services push live market data through Kafka to clients in real time — and it held up while daily users went from 15k to 200k.

15k → 200k
DAU in 14 weeks
600 → 60ms
page build + load
92%
fewer session DB calls
Go Kafka Protocol Buffers Socket.io Redis read case study →
03 2024

RAG Agents for Support & Learning

retrieval · openai · moderation

A set of RAG chatbots — support, learning, assistant — built on OpenAI APIs and grounded in real product docs. The support one cut human replies by 90%.

-90%
human support load
3
agent surfaces
Grounded
retrieval-backed answers
Node.js OpenAI APIs RAG Vector retrieval read case study →
04 2025

Durable Financial Workflows

temporal · reliability · go

Financial planning can't run on workflows that quietly break. Moving them to Temporal killed 90% of the random failures and gave the team back about three hours a day.

-90%
unexpected failures
~3 hrs/day
engineering time recovered
Durable
execution guarantees
Go Temporal MySQL read case study →

// open source

Selected open-source work.

A few favourites, pulled out of 100-plus public repos.

all projects →

free ai

An OpenAI-compatible API that spreads calls across free LLM providers and routes around whichever ones are down.

TypeScriptupdated May 2026

high signal

Pulls from SEC filings, news and Reddit, then runs an NLP pipeline to score what is actually worth knowing.

TypeScriptupdated May 2026

tinygpt

A GPT written from scratch, LoRA fine-tuning and all, that runs in the browser on WASM and WebGPU kernels.

Pythonupdated May 2026

open historia

A strategy game where you describe your nation’s moves in plain English and an LLM plays referee.

TypeScriptupdated May 2026

reel maker

1

Hand it a topic and it writes the script, makes the images, records a voiceover and renders the video.

TypeScriptupdated May 2026

stitch

Stitches a few video feeds into one panorama using SIFT feature matching and homography.

Pythonupdated Nov 2021

// expertise

Four layers I go deep on.

From the API down to storage, plus the AI parts that sit on top.

01

Backend & APIs

Getting the service right comes before getting it fast. I care about clean contracts and knowing how things break before they do.

  • Go
  • Node.js / TypeScript
  • Python
  • Protocol Buffers
  • REST APIs
  • Microservices
02

Distributed Systems & Reliability

Streaming pipelines, durable workflows, real-time delivery — the parts that decide whether a product survives getting popular.

  • Apache Kafka
  • Temporal
  • Socket.io
  • Queue-based batching
  • Docker & Kubernetes
  • Prometheus
03

AI / LLM Infrastructure

Personalized feeds, retrieval pipelines, agents. The goal is AI that holds up in production, not just in a demo.

  • RAG pipelines
  • OpenAI APIs
  • BERT embeddings
  • Milvus vector DB
  • GPT-powered features
  • Real-time vector enrichment
04

Data & Storage

Pick storage for how the data actually gets read and written. Then cache the slow paths until they stop being slow.

  • MySQL
  • PostgreSQL
  • Redis
  • ClickHouse
  • Elasticsearch
  • BigQuery

// trajectory

Where I've been.

A short version. The longer story lives on the about page.

  1. Feb 2025 — Present

    Software Engineer · VaultWealth Peak XV-backed

    Backend services and reliability infrastructure for a wealth-management platform — financial planning, durable workflows, and the systems they run on.

  2. Jan 2022 — Jan 2025

    Software Engineer · Front.Page YC S’21

    Backend and data infrastructure for a fast-growing fintech social product — real-time market data, personalized feeds, and the AI systems layered on top.

full background →