AI/ML Engineer • Full-Stack AI Developer

🚀 Backend / Full Stack Developer focused on scalable systems & APIs.
💻 Built production-ready apps including a campus social network & multi-agent AI systems.
🧠 Strong in Node.js, PostgreSQL, System Design, and AI Integrations.
🌱 Currently improving DSA & backend scalability.
⚡ Fun fact: I focus on building real-world products, not just models.

🔍 Target Roles: Backend Developer • Full-Stack Developer • AI-Focused Developer.
💡 Core Focus: Building scalable systems and integrating AI into real-world applications (LLMs, RAG, multi-agent systems).
💼 Availability: Open to full-time, internships, and contract roles.
🤝 Interests: AI-powered products, automation workflows, and production-grade system design.

Developed a comprehensive full-stack social network supporting over 2,000 universities with anonymous posting and real-time engagement.

• 🎓 Scale: Engineered location-based content filtering to deliver campus-specific feeds.
• 💬 Features: Built scalable platform mechanics including threaded comments, user tagging (@mentions), and role-based moderation.
• 🛠️ Tech Stack: Architected the backend using Next.js, PostgreSQL, and Prisma; deployed via Vercel and Supabase.

Created an advanced multi-agent AI architecture featuring intent classification and intelligent routing for billing, support, and orders.

• 🤖 AI Features: Integrated streaming AI responses in real-time to significantly lower latency and boost user experience.
• 🗄️ Architecture: Maximized database efficiency and query performance by implementing advanced connection pooling via Supabase.
• 🛠️ Tech Stack: Constructed a robust, type-safe backend infrastructure using Node.js, Hono, and PostgreSQL with Prisma.

Created an interactive conversational AI portfolio that allows recruiters to dynamically explore my skills, projects, and work history.

• 🧠 Features: Engineered a contextually aware LLM backend to deliver smart responses alongside dynamic UI rendering.
• 🛠️ Tech Stack: Implemented using Next.js, Vercel AI SDK, Groq API, and modern frontend architecture.

• AI Pest Control System: Deep learning classifier (MobileNetV2, 85%+ accuracy) that maps visual pest identification to eco-friendly pesticide recommendations. (Python, TensorFlow, OpenCV)
• Multi-Modal AI Voice Assistant: Desktop automation tool integrating YOLOv8 (vision), Whisper (speech), and NLP to execute OS commands and web searches. (Python, NLP, GenAI APIs)

⭐ If you like my work, consider giving a star!