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Open to AI infra & product roles

I build dependable AI products, from infrastructure to interface.

I'm Sarthak Agrawal, an AI infrastructure and product engineer. I turn model capabilities into systems people can trust: local code review, post-training tools, private assistants, research signals, and the operating layer behind them.

// selected work

A few things I've built.

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

01 2026

PostTrainLLM — a transformer built end to end

pytorch · webassembly · webgpu · browser

A small GPT (~0.8M params, byte-level) written from scratch in three layers — a PyTorch reference, a hand-derived C++/WASM implementation, and a full WebGPU training loop — running entirely in a browser tab. Every layer's backward pass was finite-difference checked before being trusted.

~0.8M
params, trained in-browser
24/24
GPU kernels parity-checked
1.6×
WASM SIMD speedup
PyTorch C++ WebAssembly WebGPU / WGSL TypeScript read case study →
02 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 →
03 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 →
04 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 →
05 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 →