Omar Alibi
Electrical Engineer
Embedded Systems • Edge AI • Real-Time Control • IoT Solutions
Building intelligent embedded systems from bare metal to cloud. Specialized in real-time control systems, edge AI deployment, RISC-V architecture, and industrial IoT platforms. From kernel modules to neural networks running on microcontrollers.
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About
Experience
- Engineering Intern at OnWire Link - IoT Firmware & Mobile App Development
- Real-Time Embedded Systems - BeagleBone, STM32, RISC-V SoC Design
- Edge AI & Machine Learning - TensorFlow Lite, TensorRT on Jetson Nano
- Industrial IoT - MQTT, Firebase, Real-time Dashboards & Automation
Education
- Electrical Engineering Student - ENIT (École Nationale d'Ingénieurs de Tunis)
- Preparatory Institute for Engineering Studies El Manar
- Baccalauréat in Technical Sciences - Mention Très Bien (17.43/20)
- OpusLab Frontend Development Certification
Technical Projects & Experience
Electrical Engineering student with expertise in IoT systems, embedded electronics, web development, and industrial automation. Experienced in both academic research and professional internship projects.
Developed a complete IoT ecosystem during an engineering internship, including embedded firmware for ESP8266-based smart switches, a cross-platform Flutter mobile app, and complementary tools for provisioning, monitoring, and data analysis. The system integrates hybrid online/offline control, secure authentication, and automated scheduling with solar integration.
Real-time handwritten digit recognition system running neural network inference directly on STM32 microcontroller. Features interactive Tkinter GUI for drawing digits with on-chip AI inference powered by X-CUBE-AI and TensorFlow Lite, demonstrating embedded machine learning on resource-constrained devices.
Real-time American Sign Language fingerspelling letter recognition system optimized for NVIDIA Jetson Nano edge devices. Recognizes all 26 letters using MediaPipe hand tracking, TensorFlow/TensorRT-ONNX models, and rule-based motion detection with GPU-accelerated inference.
Complete System-on-Chip (SoC) implementation featuring a RISC-V RV32I processor with single-cycle architecture, UART serial communication, GPIO controller, memory management, and auxiliary coprocessor. Designed for FPGA deployment on Zybo Zynq 7000 with full AXI-4 Lite bus integration and QuestaSim simulation support.
Core Technical Skills
Embedded Systems
Arduino, ESP32/8266, STM32, Raspberry Pi, BeagleBone, FPGA, PCB Design
Web & Software
React, Next.js, Node.js, Python, Flutter, C/C++, Rust
IoT & Industrial
MQTT, Industrial Automation, Real-time Systems
Academic Background
Electrical Engineering
National Engineering School of Tunis
2023 - Present
Professional Experience
Get In Touch
Ready to discuss your next embedded systems or IoT project?
- Embedded Systems & Real-Time Control
- Edge AI & Machine Learning Integration
- IoT Solutions & Industrial Automation
- Custom SoC & RISC-V Development