---
title: "Vignesh Bhat N - AI Engineer Intern in Bengaluru | Tal"
description: "Meet Vignesh Bhat N, AI Engineer Intern @EZEU | CSE Graduate based in Bengaluru."
image: "https://tal.club/meet/vignesh-14/share-image"
---

# Vignesh Bhat N on Tal

## Headline

AI Engineer Intern @EZEU \| CSE Graduate

## About

CSE Graduate and Software Engineering Intern at ezeu, specializing in the intersection of high\-concurrency backend systems, cloud deployment, and machine learning applications\. Current Experience: AI Engineer Intern at ezeu, focusing on scalable product development and cloud architectures\. AI & Document Intelligence \(Ex\-LogicBrackets\): Architected seiton\-ai\-ocr\-v1, integrating multi\-modal LLMs \(Qwen\-VL\) and remote PaddleOCR\-VL via vLLM to hit a 92% text reconstruction accuracy rate\. Engineered a production\-grade RAG chatbot within SeitonDMS for highly accurate document querying\. MLOps & Cloud Infrastructure: Skilled in multi\-stage Docker containerization and deploying high\-throughput, low\-latency API architectures directly onto Cloud GPUs\. Core Tech Stack: Python, FastAPI, PyTorch, Multi\-Modal LLMs, vLLM, Docker, Cloud GPUs, Vector Databases, OpenCV, Git\.

## Location

Bengaluru

## Work experience

AI Engineer Intern at EZEU \(OPC\) INDIA PVT LIMITED, Jun 2026 \- Present · 2 mos, Bengaluru, Karnataka, India

Software developer Intern at LogicBrackets, Jan 2026 \- Jun 2026 · 6 mos, Bengaluru, Karnataka, India, Scalable Architecture: Designed an object\-oriented, production\-ready FastAPI Service\-Router backend supporting asynchronous, high\-concurrency multi\-page document processing\. SeitonDMS RAG Chatbot: Engineered an advanced Retrieval\-Augmented Generation \(RAG\) chatbot within SeitonDMS, enabling high\-accuracy contextual information querying directly from drive\-uploaded documents\. Document Intelligence: Integrated PP\-Structure \(DSR\) alongside local Qwen\-VL multi\-modal models for zero\-shot entity recognition and context\-aware Key\-Value extraction, achieving a 92% layout reconstruction accuracy rate\. Optimization & Deployment: Containerized backend services utilizing multi\-stage Docker builds and deployed the infrastructure onto Cloud GPUs, leveraging the vLLM framework and custom LRU caching to eliminate CUDA memory leaks\.

## Education

Bachelor of Engineering \- BE, Computer Science, Bapuji Institute of Engineering & Technology, Davanagere

Science, Sir MV PU college

[View the original profile](https://tal.club/meet/vignesh-14)

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