class RudraChothe:
    def __init__(self):
        self.name        = "Rudra Chothe"
        self.location    = "India 🇮🇳"
        self.degree      = "B.Tech CSE (AIML) @ RIT Islampur — '26 | CPI: 8.13"
        self.focus       = ["AI/ML Systems", "RAG Pipelines", "Full-Stack Engineering"]
        self.languages   = ["Python", "TypeScript", "JavaScript"]
        self.currently   = "Building production-grade AI systems and backend APIs"
        self.open_to     = "Remote full-time roles & freelance AI/backend projects"

    def contact(self):
        return "rudrac.0410@gmail.com"

I build AI systems that run in production — not just in notebooks. RAG pipelines, LLM-powered apps, multimodal voice assistants, face authentication APIs, and CI/CD automation. I'm most comfortable at the intersection of AI/ML and backend engineering: designing systems where models don't just work, they scale.

• 🔬  Specialized in RAG, LLMs, Agentic AI, and Computer Vision
• ⚙️  Backend-heavy: FastAPI, Node.js, Express, Flask — REST APIs at scale
• 📱  Cross-platform: React, React Native for web and mobile
• 🤖  Automation obsessed: n8n, GitHub Actions, CI/CD pipelines
• 🏆  Hackathon winner. Open source contributor. Always building something.

Languages

AI / ML

Backend

Frontend & Mobile

Databases & Cloud

DevOps & Automation

ResNet50-based skin disease classification with a full RAG pipeline for auto-generating clinical reports.

• Achieved multi-class skin disease classification using ResNet50 fine-tuned on dermatology datasets
• Built a RAG pipeline that pulls from embedded clinical knowledge to auto-generate context-aware medical reports
• Integrated an interactive medical chatbot for real-time patient queries
• Cut manual diagnosis effort by ~60% through end-to-end automation
• Python TensorFlow RAG LLMs ChromaDB Firebase React

🏆 SGU Hackathon Winner. Full-stack hospital platform with AI-powered triage.

• Role-based access for doctors, patients, and receptionists with secure JWT authentication
• Appointment scheduling, medical record tracking, and automated report generation
• Integrated LLM-powered chatbot for smart triage and real-time admin queries
• Improved hospital workflow efficiency by ~45% by digitizing core operations
• Node.js React MongoDB Firebase Gen-AI

Full speech-to-text → retrieval → LLM reasoning → text-to-speech pipeline. Multimodal AI, end to end.

• Complete STT → RAG → LLM → TTS pipeline using Whisper-style speech recognition
• Chroma-based vector retrieval with dynamic org-specific RAG chains
• Real-time inference APIs powered by Gemini and Flask
• Python LLMs RAG ChromaDB Flask Gemini STT/TTS

agent-skills — Callstack Incubator  ·  React Native Build Systems

Built and merged an Android native library validation system for React Native apps (Android 14 alignment issues). Combines pre-build checks and ELF binary inspection. Now used in production Play Store deployments.

NeatNode — CLI Tool  ·  Backend & CI/CD

Fixed critical runtime bugs across middleware/controllers and implemented deterministic template handling with GitHub Actions CI/CD pipelines. Improved API reliability and DX.

I'm open to remote full-time roles, freelance AI/backend projects, and open source collaboration.