---
title: "Rikesh Sharma - Software Engineer in Chennai | Tal"
description: "Meet Rikesh Sharma, Software Engineer | AI & ML Engineer | Full Stack Developer | Python | ReactJS | TensorFlow | LLM & RAG Applications. based in Chennai. AI stack includes gemini, langchain, and ollama."
image: "https://tal.club/meet/rikesh/share-image"
---

# Rikesh Sharma on Tal

## Headline

Software Engineer \| AI & ML Engineer \| Full Stack Developer \| Python \| ReactJS \| TensorFlow \| LLM & RAG Applications\.

## About

Software Engineer and AI/ML Engineer with hands\-on experience in full\-stack web development, machine learning model engineering, and production\-grade application deployment\. I build and ship scalable systems — from real\-time chat applications handling 50\+ concurrent users at sub\-50ms latency, to deep learning pipelines achieving 92%\+ accuracy on 54,000\-image datasets\. 🔧 Tech Stack: \- Languages: Python, JavaScript, TypeScript, Java, C\+\+, SQL \- Web: ReactJS, Node\.js, Express\.js, FastAPI, Flask, Socket\.io \- AI/ML: TensorFlow, Keras, Scikit\-learn, CNNs, Transfer Learning \- Generative AI: LLM Apps, RAG Architecture, OpenAI APIs, LangChain, Hugging Face \- Cloud & DevOps: AWS, Vercel, Docker, GitHub Actions, CI/CD \- Databases: MySQL, MongoDB, Firebase, Vector Databases 🎯 Currently pursuing B\.Sc\. Computer Science \(AI & ML specialisation\) at Vels University, graduating May 2026\. 📩 Open to entry\-level Software Engineering or AI/ML Engineering roles at 6–12 LPA\. Reach me at: Senthilethiraj68@gmail\.com

## Location

Chennai

## AI stack

gemini

langchain

ollama

openai

## Work experience

Software Engineer at Self\-Employed, Jun 2023 \- Present · 3 yrs 1 mo, 1\. Completed more than 5 end\-to\-end Generative AI projects applying Prompt Engineering, LLM integration, and RAG architecture using OpenAI APIs, Hugging Face, and LangChain\. 1\. Solved more than 100 Data Structures and Algorithms problems on competitive programming platforms, reinforcing system design and algorithmic thinking for production engineering roles\. 1\. Grew an active open\-source portfolio to more than 10 repositories with over 500 commits, covering the full software lifecycle from architecture design through to cloud deployment\.

Machine Learning Engineer at Freelance, Aug 2024 \- Nov 2024 · 4 mos, 1\. Trained a VGG16\-based CNN image classifier using transfer learning on the PlantVillage dataset comprising 54,000 images across 38 plant disease categories\. 1\. Exceeded baseline model performance by 35 percent, reaching over 92 percent validation accuracy through systematic hyperparameter tuning, dropout regularisation, and data augmentation across 5 evaluation metrics\. 1\. Slashed model inference latency by 60 percent to under 200ms by optimising the prediction pipeline, enabling real\-time deployment in low\-resource agricultural field environments\. 1\. Accelerated model training by 20 percent by parallelising the data ingestion and preprocessing pipeline using Python, TensorFlow, Keras, NumPy, and Pandas\. 1\. Produced diagnostic reports including confusion matrices, per\-class accuracy tables, and loss curves using Matplotlib, covering all 38 disease categories for stakeholder review\. 1\. Released fully reproducible training scripts and model documentation on GitHub, enabling any engineer to replicate results within a single environment setup\.

Full Stack Engineer at Freelance, Jan 2024 \- Apr 2024 · 4 mos, 1\. Spearheaded development of a production\-grade real\-time chat application using the MERN stack, achieving message delivery latency under 50ms for more than 50 concurrent users\. 1\. Deployed WebSocket infrastructure using Socket\.io with real\-time event broadcasting and room\-based routing, sustaining stable throughput across 3 production environments\. 1\. Secured the API layer with JWT authentication, bcrypt password hashing, and role\-based session management, passing more than 100 security test cases with zero vulnerabilities\. 1\. Slashed UI re\-renders by 40 percent and improved page load speed by 30 percent by redesigning the ReactJS component architecture with optimised state management patterns\. 1\. Automated deployment using Vercel CI/CD pipelines, reducing release time to under 10 minutes per cycle and sustaining near\-perfect uptime throughout the 4\-month project\. 1\. Streamlined team onboarding by producing comprehensive README documentation, API specifications, and architecture diagrams in a well\-structured GitHub repository\.

## Education

Artificial Intelligence, Vels University

[View the original profile](https://tal.club/meet/rikesh)

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