A premium, interactive, and highly optimized portfolio website designed for an AI/ML Engineer. Featuring custom animations, a dynamic glassmorphic UI, an interactive AI Guide ("Aria"), and deep technical sections highlighting machine learning projects and data science expertise.
- Dynamic Hero Section: Canvas-based interactive particle network using pure Math/Canvas (no external 3D libraries required) with animated stat counters.
- AI Guide "Aria": A custom CSS-animated guide character that tracks your scroll position and provides contextual messages for each section.
- Targeted Resume Selector: A sleek interactive modal that allows recruiters to download specific resumes tailored for AI Engineering, ML Engineering, or Data Science.
- ML Pipeline Visualizer: A step-by-step visual representation of End-to-End ML and Agentic AI workflows.
- Glassmorphic UI: Modern frosted-glass aesthetic applied to cards, navigation, and modals.
- Responsive & Optimized: 100% mobile responsive with lazy-loading for images and Open Graph (OG) meta tags for professional social sharing.
- Framework: React 18 + Vite (Blazing fast development server)
- Styling: Pure CSS with CSS Variables (Design Tokens) for easy theme management
- Animations:
- Framer Motion (Component mounting & layout animations)
- GSAP & Lenis (Smooth scrolling & scroll-triggered effects)
- Interactive Elements: HTML5 Canvas (Particle Network)
portfolio/
├── public/
│ ├── certificates/ ← PDF certificates and credentials
│ ├── pdf_models/ ← Targeted Resumes (AI, ML, Data Science)
│ └── images/ ← Profile pictures and project screenshots
├── src/
│ ├── assets/
│ │ └── global.css ← Global design tokens, typography, and utility classes
│ ├── components/
│ │ ├── Navbar.jsx ← Responsive top navigation
│ │ ├── Footer.jsx ← Minimalist branded footer
│ │ ├── GuideGirl.jsx ← Animated AI guide "Aria"
│ │ ├── CustomCursor.jsx ← Custom cursor implementation
│ │ └── BackToTop.jsx ← Floating scroll-to-top button
│ ├── sections/
│ │ ├── HeroSection.jsx ← Particle canvas, animated stats, intro
│ │ ├── AboutSection.jsx ← Terminal-styled about me
│ │ ├── SkillsSection.jsx ← Technical skills grid
│ │ ├── PipelineSection.jsx ← ML/AI workflow methodology
│ │ ├── ProjectsSection.jsx ← Project case studies
│ │ ├── ExperienceSection.jsx ← Professional experience
│ │ ├── CertificatesSection.jsx ← Credentials with PDF links
│ │ ├── TimelineSection.jsx ← Educational/Professional journey
│ │ └── ContactSection.jsx ← Contact form & Resume selector
│ ├── data/
│ │ └── portfolioData.js ← ⭐️ CENTRALIZED DATA: Edit this file to update content!
│ └── App.jsx ← Main application layout
├── index.html ← HTML entry point & OG Meta Tags
└── package.json
Make sure you have Node.js installed on your machine.
-
Clone the repository:
git clone https://github.com/rupakkumar-76319/portfolio_v2.git cd portfolio_v2/portfolio -
Install dependencies:
npm install
-
Start the development server:
npm run dev
The app will run at
http://localhost:5173/ -
Build for production:
npm run build
This will generate a highly optimized
distfolder ready for deployment.
The portfolio is designed to be easily updatable without digging through complex component code. Almost all content is driven by a single file: src/data/portfolioData.js.
Change your name, role, email, and social links in the personalInfo object.
Find the projects array and add your latest work:
{
id: 1,
title: "GitHub Intelligence MCP Server",
desc: "An AI-powered GitHub MCP Server...",
category: "ai-eng",
tags: ["AI", "Python", "MCP", "LLM", "GitHub API"],
github: "https://github.com/...",
demo: "#",
status: "in-progress",
}Place your new PDF resumes inside public/pdf_models/Resume/. Ensure the filenames match the links specified in ContactSection.jsx.
This project is optimized for effortless deployment on platforms like Vercel, Netlify, or GitHub Pages.
Deploying to Vercel (Recommended):
- Push your code to GitHub.
- Log into Vercel and click "Add New Project".
- Import your GitHub repository.
- Leave the default build settings (
Framework Preset: Vite,Build Command: npm run build,Output Directory: dist). - Click Deploy.
This project is open-source and available under the MIT License. Feel free to fork, customize, and use it for your own portfolio!
Built by Rupak Kumar for the AI/ML Engineering community.