Role : Software Engineer @ FIFA 2026
Focus : Full-Stack Systems | Cloud Architecture | AI Applications
Location : Miami, FL
Tech: React, Node.js, OpenAI API, RAG
AI-powered full-stack application that enables natural language card search through intelligent query translation and scalable retrieval workflows.
| Feature | Details |
|---|---|
| 🤖 AI Search | Uses LLM-driven prompt design to translate natural language into card search logic. |
| 🧠 RAG Pipeline | Uses embeddings and vector-based retrieval for scalable semantic search. |
| 🔗 API Integration | Connects backend services with external card data APIs for real-time results. |
| 🏗️ Full-Stack Design | Built with a React frontend and Node.js backend for extensibility. |
Tech: Python, OpenCV, MediaPipe
Computer vision project for tracking body movement, analyzing exercise form, and generating real-time feedback from pose estimation data.
| Feature | Details |
|---|---|
| 📹 Pose Tracking | Detects and tracks skeletal keypoints from video input. |
| 📊 Motion Analysis | Evaluates movement quality and exercise consistency. |
| ⚡ Real-Time Feedback | Designed for live form-checking workflows. |
| 🧠 AI Foundation | Serves as a base for future intelligent coaching applications. |
Tech: React, Node.js, PostgreSQL
Full-stack trading analytics application for tracking performance metrics and visualizing portfolio-level results.
| Feature | Details |
|---|---|
| 📉 Performance Metrics | Tracks P/L, drawdown, and profit factor. |
| 🔌 Backend APIs | Handles calculations, trade management, and persistent storage. |
| 🗄️ Relational Data | Uses PostgreSQL for structured trade and account data. |
| 📊 Responsive UI | Visualizes performance in a clean, user-facing interface. |