Rayan Mustafa | Emebedded System Engineer

Hi, I'm Rayan Mustafa.

A

A passionate embedded system engineer with skills in C/C++, Python, Javascript/Typescript. Currently Pursuing a degree in Electrical and Computer Engineering at University of Toronto

About

I am a Computer Engineer Undergraduate Student at University of Toronto. I enjoy projects involving both hardware and software. Ready to learn any new skill required for these projects. I currently develop in Python, Flask, HTML5, CSS, Javascript/Typescript, C/C++, Rust. I have 16 months of professional work experience which helped me strengthen my experience in Python, C/C++, Javascript/Typescript. I am passionate about developing embedded system projects that can be physically used in several situations.

Languages: C, C++, Python, JavaScript, Bash, HTML, CSS, TypeScript, Angular, Django, GO, Bash
Databases: MySQL, PostgreSQL
Libraries: NumPy, Pandas, Scikit-learn
Developer Tools: Git, SVN, Putty (SSH), Doxygen, Visual Studio Code, GitHub, Bitbucket, JIRA
Frameworks: Flask, Django, Node.js, Angular, PyTorch, Bootstrap,

Looking for an opportunity to work in a challenging environment to force me to learn new skills and Technology. I interested to work on Embedded System, Firmware, Machine Learning & AI

Experience

Evertz Microsystems

Embedded Software Engineering Intern

Built C++/WebSocket service to program Analog-to-Digital-Converts (ADCs) to expose RF data to front-end application and enable RF over IP
Developed C/C++ Websocket testing tools to reduce develop debugging time by 20%
Designed OOP-based C++ network packet parser to facilitate flexibility and rapid feature development
APrototyped ADC firmware dashboard in Figma, accelerating UI dev start by 4 weeks
Authored system architecture documents to speed onboarding and simplify development
Built ARM C++ toolchain VM to provide a flexible, accessible development environment for remote teams
Developed Python and C/C++ testing tools for FPGA drivers to automate driver development and testing
Implemented front-end features for the ADC firmware web interface using Angular/TypeScript, improving UI responsiveness
Built FFmpeg C application for network media streaming and remuxing
Tools: C/C++, Figma, Python, JavaScript/TypeScript, Angular, FFmpeg, Virtual Machines, Doxygen

May 2024 - August 2025 | Burlington, Canada

University of Toronto

FPGA Research Intern

Ported network intrusion detection system (Pigasus) from Intel Quartus to Xilinx Vivado, retaining 95% functionality and meeting timing constraints
Rewrote Quartus pre-packages design modules in SystemVerilog and Verilog for compatibility with Vivado
Rewrote multiple modules in SystemVerilog and Verilog to improve signal integration between components
Used Vivado and ModelSim/Questa to validate components, achieving 90% functional and timing parity between rewritten and original modules
Tools: Python, Flask, OpenCV, Keras, Tensorflow, PyTorch

May 2023 - August 2023 | Toronto, Canada

Projects

                
Robotic hand controlled by glove
                  A person can move their hand wearing glove and the robotic hand will mimic the movement.
                
AccomplishmentsTools: I2C, C, STM32CUBE
Developed I2C drivers and Bluetooth communication using C achieving under 200 ms response time, enabling
smooth servo operation across 5 accelerometers and 5 servos
Configured I2C and PWM pins and peripherals in STM32Cube, generating HAL APIs for accurate setup and
accelerated development
Worked with Keil µVision and STM32Cube to efficiently compile and flash firmware onto microcontrollers

Home Lab
                  Using a mini-pc, deployed a home server with multple essential server such as Network-Attached Storage using SMB & Password Manager.
                
AccomplishmentsBuilt a secure home lab environment using Docker for hosting self-managed services with high availability
Configured Nginx Proxy Manager with reverse proxy rules and an internal DNS resolver to route domains to
correct internal IPs and enable HTTPS connections
Integrated Tailscale VPN to allow secure, remote access from any device outside the local network

Garbage Classification ML model 
                  A Convolutional Neural Network Model Developed using Pytorch to classify picture of garbage
                
AccomplishmentsTools: Python, PyTorch, Pandas, NumPy 
Built and tuned CNN using PyTorch achieving 95% accuracy, outperforming baseline by 10%
Preprocessed datasets with Python, removing 20% low-quality images and augmenting data to increase training
samples by 50%
Utilized NumPy and Pandas to efficiently organize, categorize, and preprocess datasets for optimized CNN
training