---
title: "AKHIL PRASANNAN - Associate Technical Architect in Bengaluru | Tal"
description: "Meet AKHIL PRASANNAN, AI Engineer | Google · Quantiphi · Spyne | From Computer Vision to Agentic AI — shipping LLM-powered systems at scale based in Bengaluru. AI stack includes github copilot and langgraph."
image: "https://tal.club/meet/akhil-prasannan/share-image"
---

# AKHIL PRASANNAN on Tal

## Headline

AI Engineer \| Google · Quantiphi · Spyne \| From Computer Vision to Agentic AI — shipping LLM\-powered systems at scale

## About

I'm a Full\-Stack AI Engineer who has spent the last 7\+ years building intelligent systems across Computer Vision, Generative AI, and Agentic workflows — and shipping them into production environments where they actually have to work\. At Google, I built and deployed ML pipelines on Vertex AI\. At Quantiphi, I architected end\-to\-end AI solutions for enterprise clients — from transformer\-based classifiers trained on 500K\+ instance datasets to production RAG systems and agentic workflows powered by LLMs\. I've worked across the full stack: data pipelines, model development, cloud deployment \(AWS \+ GCP\), and the messy integration work that most "AI engineers" skip\. What I've learned in the process: → The gap between a working model and a working product is where most teams bleed time and money → Computer Vision \+ GenAI \+ Agents aren't three separate disciplines — the most powerful systems combine them → Architecture decisions made in week one determine whether you're iterating or rewriting in month six Right now, I'm focused on agentic AI systems — designing agents that reason, use tools, and operate reliably in production\. I'm also starting to write about what I'm building and learning in this space\. If you're working on something interesting at the intersection of vision, GenAI, and agents — or you just want to talk shop — send me a message\. Always happy to connect with builders\.

## Location

Bengaluru

## AI stack

github copilot

langgraph

## Work experience

Associate Technical Architect at Quantiphi, Feb 2026 \- Present · 7 mos, Bengaluru, Karnataka, India

Software Engineer \(XWF VIA ADECCO\) at Google DeepMind, Jun 2024 \- Dec 2025 · 1 yr 7 mos, Bengaluru, Karnataka, India

Senior Machine Learning Engineer 3\.0 at Quantiphi, Jul 2023 \- Jun 2024 · 1 yr, Remote, ●RAG LLM system on AWS \(Generative AI\): Deployed an RAG LLM system on AWS using AWS Bedrock, OpenSearch, and LangChain optimizing natural language understanding and information retrieval for enhanced user experiences and data\-driven decision\-making\. ● Natural Language to SQL: Deployed multiple projects for natural language to SQL generation using AWS Athena,Bedrock and Opensearch\.

Machine Learning Engineer at Quantiphi, Aug 2021 \- Jul 2023 · 2 yrs, Bengaluru, Karnataka, India, Large Scale Image Classification models \(Computer Vision\): Trained and deployed multiple variations of CNNs and Transformer based networks for 2000 plant classification and 10000 animal classification\. Achieved 3X faster training with the same model architecture and training hardware configurations\. ● Multi\-node Training: Implemented multi\-node training on AWS Sagemaker for custom Deep learning models\. ● Intent Identification and Entity Extraction\(NLP\): Implemented intent identification and entity extraction module using both inbuilt aws comprehend and pytorch\. ● Image Segmentation POC \(Computer Vision\): Implemented Image Segmentation for detecting the type of underground optical fibers\. Used multiple versions of Unet and a modified version of U2net

Early Team Member at Upscalepics, Nov 2020 \- Aug 2021 · 10 mos, Bengaluru, Karnataka, India

Computer Vision Engineer at Spyne, Mar 2020 \- Aug 2021 · 1 yr 6 mos, Gurugram, Haryana, India, ● Implemented Entire Backend for Inference: Developed and deployed diverse AI services Background\-removal\(Saliency Detection\)/replacement, Image tagging, Shadow generation on multiple cloud services\(Amazon AWS, Google cloud and Microsoft Azure\) with focus on performance\. ● Human Parsing for Automated Image Cropping : Built and deployed a deep learning module using Pytorch ,Opencv for cropping images for E\-commerce and deployed on Azure\. Usage of this module saved an approximate additional 30% of an editor's time\. ● Multiple POCs on Object Detection,Segmentation, Image Clustering,2D to 3d generation using NERF\.

## Education

Bachelor of Technology \- BTech, Computer Science, University School of Information And Communication Technology, GGS IndraPrastha University

Coursera \- DeepLearning Specialization By Andrew NG

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

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