---
title: "Anubhav Agrawal - AI Engineer - 2 in Bengaluru | Tal"
description: "Meet Anubhav Agrawal, Making ML models run fast on real hardware | ONNX · TensorRT · INT8/FP16 Quantization · llama.cpp/MLX On-Device Inference | AI Engineer @ Griphic based in Bengaluru. AI stack includes claude code, hugging face, and kilocode. Explore public work links including github and anubh..."
image: "https://tal.club/meet/anubhav-agrawal-2/share-image"
---

# Anubhav Agrawal on Tal

## Headline

Making ML models run fast on real hardware \| ONNX · TensorRT · INT8/FP16 Quantization · llama\.cpp/MLX On\-Device Inference \| AI Engineer @ Griphic

## About

I make deep\-learning models run in real time — on GPUs and on\-device hardware\. My work is everything after training: PyTorch → ONNX → TensorRT export, INT8/FP16 quantization, runtime profiling, and serving\. It has shipped in two very different products — a multi\-agent LLM service in production, which I own as its sole engineer, and an X\-ray enhancement product taken through clinical trials to release — with a granted Indian patent and two more pending along the way\(hopefully\!\), and a stretch leading an ML team\. The details and numbers are in the roles below\. The models I have shipped see \(X\-ray imaging\) and read and write \(LLMs\)\. The next ones I want listening and watching — on\-device\. In my own time I benchmark LLM inference engines\. I built a reproducible harness that holds mlx\-lm and llama\.cpp to one fairness contract on an M4 Pro, and measured two things their docs do not say: decode is memory\-bandwidth\-bound \(\~273 GB/s ceiling\), and K isolated server instances return \+9% throughput for 3× the memory\. I also built ipic, a fully offline multimodal search engine that indexes a 350k\-file drive at \~2,500 files/s\. I contribute upstream where I work: a merged fix in ONNX Runtime's graph optimizer \(\#33091\), and open PRs in PyTorch's ONNX exporter \(\#200180\) and mlx\-lm's server \(\#1944\)\. M\.Tech AI/ML @ BITS Pilani, 2027\. Write\-ups: anubhavagr\.github\.io\. If your team is working on something interesting, I'd love to hear from you: anubhavagr\.mail@gmail\.com\.

## Location

Bengaluru

## AI stack

claude code

hugging face

kilocode

openai

llama\.cpp

vLLM

## Work experience

AI Engineer \- 2 at Griphic, Jan 2026 \- Present · 11 mos, Delhi, India, Working on multi\-agent LLM service in production \(Python/FastAPI\) — designed, built, shipped, and maintained by one person\. I cut per\-request generation latency cost by 60% by implementing token tracing, prompt caching, and bounding parallel fan\-out\. Outputs are safe to ship unread: 99\.5% go out with zero human review, and a two\-stage guardrail — a batched judge, then an independent adjudicator — stops prompt injection and credential leaks for at most 2 extra LLM calls per clean request\. Every merge clears unit \+ BDD suites, SonarQube, and Trivy scans\.

Machine Learning Engineer at Innvolution Healthcare Private Limited, Sep 2023 \- Dec 2025 · 2 yrs 4 mos, Bengaluru, Healthcare ML with real stakes: images from live cardiac procedures, clinical trials, regulators watching\. I owned AIMAG — X\-ray super\-resolution and denoising, $200K\+ ARR — from dataset curation through release after clinical trials\. The part I actually loved was the part nobody posts about: dragging PyTorch models through ONNX export \(dynamic shapes, unsupported ops, opset mismatches\) into TensorRT with INT8/FP16 quantization, landing at sub\-5 ms per frame for 4× upsampling on an RTX 4090\. Also fine\-tuned U\-Net\+\+ \(ResNet50 \+ attention\) for coronary segmentation running in under 4 seconds in the cath lab, co\-invented a granted patent \(No\. 604176\) and two pending applications in X\-ray enhancement, and led a team of engineers— roadmap, code review, deployment runbooks\.

Image Quality Engineer \(Machine Learning\) Intern at Innvolution Healthcare Private Limited, Dec 2022 \- May 2023 · 6 mos, Bangalore, Motion\-compensated image stitching and denoising for Digital Subtraction Angiography \(DSA\) images

Computer Vision Intern at enord, Sep 2022 \- Dec 2022 · 4 mos, Delhi, India

Research Intern at ISRO \- Indian Space Research Organization, Jun 2022 \- May 2023 · 1 yr, Remote, Time\-series forecasting \(XGBoost/CatBoost/LightGBM\) on NDVI \(Normalized Difference Vegetation Index\) data from AWiFS, MODIS and Sentinel satellites\.

## Education

Master of Technology \- MTech, Artificial Intelligence and Machine Learning, BITS Pilani Work Integrated Learning Programmes

Bachelor of Technology \- BTech, Computer Science and Engineering, Bhilai Institute of Technology \(BIT\), Durg

## Public links

- github: https://github\.com/anubhavagr
- anubhavagr\.github\.io: https://anubhavagr\.github\.io/

[View the original profile](https://tal.club/meet/anubhav-agrawal-2)

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