I am an aspiring Software Development Engineer with a passion for building scalable systems. My journey in computer science has equipped me with strong analytical skills and a solid foundation in software engineering. I thrive on challenges and am always eager to learn new technologies to stay ahead in this fast-paced digital world.
My experience includes working on innovative projects in AI and full-stack development, where I’ve implemented various data structures, databases, and APIs. I enjoy collaborating with teams and using my skills to create solutions that make a difference.
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A multi-agent AI system designed for real-time monitoring, anomaly detection, and alert orchestration to improve elderly safety and healthcare response.
- Python-based backend service
- LLM API integration for dynamic response generation
- Sentiment analysis pipeline for contextual adaptation
- RESTful API endpoints for scalable access
- Structured logging and validation layer
- Modular architecture separating routing, business logic, and AI processing
- Designed the system to be stateless for scalability
- Implemented a reusable API response wrapper for consistency
- Structured the AI orchestration layer to allow easy model upgrades
- Focused on clean architecture and separation of concerns
LumiPsyche is an AI-powered conversational backend system designed to deliver context-aware and sentiment-adaptive responses using LLM integration.
🔹 Problem
Most AI chat systems remain prototype-level and lack structured backend architecture. The goal was to design a modular, scalable AI system closer to production standards.
🔹 What I Built
🔹 Key Technical Decisions
🔹 Impact
The project evolved from a simple chatbot concept into a structured backend service capable of handling concurrent requests and extensible AI workflows.
🔹 Tech Stack
Python, NLP, LLM APIs, REST APIs, JSON, Git
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