const kallappa = {
    pronouns: "He/Him",
    education: "B.Tech Computer Science @ REVA University (2023–2027)",
    location: "Bengaluru, India 🇮🇳",
    intro: "CS undergraduate building full-stack applications, AI/ML applications, and deploying with MLOps & DevOps practices.",
    interests: [
        "Backend Systems & Scalable REST APIs",
        "End-to-End ML Pipelines using MLOps",
        "Cloud Deployments with Docker, Kubernetes, CI/CD & AWS"
    ],
    currentlyLearning: ["System Design", "Microservices", "DSA", "Agentic AI"],
    funFact: "I debug faster when the deadline is closer! ⚡",
};

Duration: 6 Months (Paid Internship)  |  🔗 Live Demo

• Developed and maintained full-stack features across the research portal using the MERN stack
• Architected and implemented the backend system powering core application modules with clean, modular code
• Performed query optimizations and database indexing on MongoDB, significantly improving application performance
• Built scalable REST APIs with Express.js, improving response times and ensuring consistent API contracts
• Collaborated with the team via GitHub — raising PRs, reviewing teammates' code, and ensuring smooth merge workflows
• Implemented JWT-based authentication and role-based access control for secure user sessions
• Contributed to backend architecture decisions and participated in sprint planning and code reviews

• Contributed to 2+ open source projects as part of GSSoC 2026
• Studied and understood the existing backend systems of both projects before contributing
• Identified gaps and created 7+ issues covering backend enhancements, bug reports, and feature suggestions
• Picked up existing issues raised by project admins for backend improvements, fixed them, and raised 8+ PRs — all successfully merged

• Selected as Project Admin for my own open source project under GSSoC 2026
• Designed and created starter issues and leveled issues (Easy / Medium / Hard) for contributors to onboard smoothly
• Reviewed and merged 40+ PRs, ensuring code quality and consistency across contributions
• Collaborated with like-minded contributors and fellow project admins to maintain project momentum and community health

🔗 GitHub Repo

graph TB
    A[🎓 Current Focus] --> B[🌐 Full Stack Development]
    A --> C[🤖 Machine Learning]
    A --> D[⚙️ MLOps]
    A --> E[☁️ DevOps]
    
    B --> B1[Frontend Mastery]
    B --> B2[Backend Development]
    B --> B3[Database Design]
    
    C --> C1[Deep Learning]
    C --> C2[Model Building]
    C --> C3[Data Analysis]
    
    D --> D1[DVC & MLflow]
    D --> D2[Docker Containers]
    D --> D3[CI/CD Pipelines]
    
    E --> E1[Linux & Git]
    E --> E2[Kubernetes]
    E --> E3[AWS Cloud]
    E --> E4[Prometheus]
    E --> E5[Grafana]
    E --> E6[Loki]
    E --> E7[Terraform]
    E --> E8[Networking]
    
    style A fill:#ff6b6b,stroke:#c92a2a,stroke-width:3px,color:#fff
    style B fill:#4ecdc4,stroke:#006d77,stroke-width:2px,color:#000
    style C fill:#ffe66d,stroke:#f4a261,stroke-width:2px,color:#000
    style D fill:#a8dadc,stroke:#457b9d,stroke-width:2px,color:#000
    style E fill:#f1faee,stroke:#1d3557,stroke-width:2px,color:#000

Full-featured e-commerce platform with separate Admin and User dashboards

• Users can browse products, manage their cart, place orders, and pay via Stripe, Razorpay, or Cash on Delivery
• Real-time order tracking — users see live status updates set by admins
• Admins can perform full CRUD operations on products and manage order statuses for all users

🔗 GitHub Repo

End-to-end MLOps pipeline for wine quality classification

• Trained a classification model using scikit-learn on the red wine dataset
• Implemented DVC for data versioning and pipeline reproducibility
• Exposed predictions via a Flask REST API with clean endpoints
• Fully Dockerized, with GitHub Actions CI/CD and deployed on Render

🔗 GitHub Repo

Deep learning + MLOps pipeline for medical image classification

• Fine-tuned VGG16 CNN on a kidney disease dataset for multi-class classification
• Integrated MLflow for experiment tracking and DVC for pipeline management
• Served via a Flask REST API and deployed via Dockerized GitHub Actions CI/CD on Render

🔗 GitHub Repo

Enterprise-grade employee management system with full DevOps implementation

• Built Employee and Admin dashboards with role-based access and management features
• Added email notifications via Nodemailer (AWS SNS integration)
• Wrote unit and integration tests using Supertest & Jest
• Dockerized the full application and orchestrated with Kubernetes
• Set up Prometheus, Grafana & Loki for monitoring, metrics, and log aggregation
• Managed infrastructure with Terraform (IaC) and deployed to AWS using EKS, S3, and EC2 via GitHub Actions CI/CD

🔗 GitHub Repo

AI-powered job fraud and scam detection platform

• Built with React (frontend) and Python/Flask (backend) to screen suspicious job postings, recruiter messages, and email/domain signals
• Classifies job listing text using a BERT-based NLP model
• Inspects domains using WHOIS, DNS, PhishTank-style lists, SSL certificate quality, and domain mismatch signals
• Stores repeated entities and fraud patterns in a Neo4j knowledge graph
• Integrated Google Gemini for advanced AI-assisted analysis

🔗 GitHub Repo

Developer-focused Python voice assistant with AI integrations

• Built with Python 3, designed specifically for learners and developers
• Supports browser operations, navigation, speech navigation, and AI Code Security Analysis
• Integrated Gemini API and LLaMA for intelligent AI responses
• Features VS Code automation — creates project scaffolding for different frameworks, sets up directory structures, and runs projects via voice commands

🔗 GitHub Repo