---
title: "About | Harshith Nayaka L"
description: "Harshith Nayaka L — AI Engineer, Full-Stack in Bengaluru (Bangalore), India. AI Workflow Engineer at DemandNXT. Available for freelance work."
canonical: "https://harshith-nayaka-l-portfolio.vercel.app/about"
last-updated: "2026-09-22T07:59:51Z"
author: "Harshith Nayaka L"
author-role: "AI Engineer, Full-Stack"
author-title: "AI Workflow Engineer"
author-location: "Bengaluru, India"
author-availability: "Available for freelance work"
author-email: "harshith28124@gmail.com"
content-type: "text/markdown"
html-version: "https://harshith-nayaka-l-portfolio.vercel.app/about"
---
# About — Harshith Nayaka L

> Harshith Nayaka L — AI Engineer, Full-Stack in Bengaluru (Bangalore), India. AI Workflow Engineer at DemandNXT. Available for freelance work.

Canonical page: https://harshith-nayaka-l-portfolio.vercel.app/about

Harshith Nayaka L is a full-stack AI Engineer based in Bengaluru (Bangalore), Karnataka, India, and goes by AI Workflow Engineer at DemandNXT.

## The short version

Role: AI Engineer, Full-Stack. Company: DemandNXT, where the title is AI Workflow Engineer. Based in Bengaluru, India. Building since 2022. Available for freelance work.
The work is AI agents, retrieval pipelines and full-stack AI applications — the model, the backend, and the interface around them. At DemandNXT that means production AI systems and pipelines for marketing and creative operations.

## What the work actually is

Most of the engineering in an AI system is not the model. It is everything that assumes the model can be wrong: routing a request before spending on it, constraining output to a schema and validating it anyway, having a second model on a different family verify the first, isolating agents so context cannot leak between them, and keeping a decision log that can be replayed after something goes wrong.
That is the difference between a demo that works once and a system a business can lean on, and it is most of the job. There are 11 case studies on this site and each one is a version of that argument with the code attached.

## The honest version

Building with code since 2022. That is a few years of shipping things, breaking them and learning what reliable looks like up close — not years of production ML research, and this site does not claim otherwise.
Everything stated here is checkable. The projects are public repositories, the research is a published paper with a DOI, and where a number appears it traces to something that can be read rather than taken on trust.

## Published research

Author of “AI-Powered Note-Taking System: A Local Machine Learning Approach DeepSeek R1 Integration”, published in the International Journal of Research Trends and Multidisciplinary Research (IJRTMR), Nov–Dec 2025. It documents the local-inference approach behind the AI Notes project: summarisation running on DeepSeek R1 through Ollama, with no cloud fallback.