Lyba Mughees

Hi, I'm Lyba Mughees

Cloud Automation Developer @ Bell Canada

Lyba Mughees

See my work Get in touch

About Me

Software Engineer & Cloud Automation Developer pursuing an MMAI at Queen's University, specializing in cloud automation, infrastructure modernization, and scalable enterprise solutions driven by AI and data.

Experience

Bell Canada — Cloud Automation Developer I April 2025 - Present

Led migration planning for VMware → OpenStack (≈610 servers), recovered ESXi hosts via VM optimizations, and delivered multimillion-dollar savings. Produced runbooks and support playbooks.

Bell Canada — Software Developer May 2024 - April 2025

Built automation with PowerShell for migration workflows; implemented cost-optimization tooling and license automation.

Bell Canada — Software Development Intern Summer 2022 / Summer 2023

Redesigned internal portals, added SQL logging for audits, and improved internal tooling using C# and JavaScript.

Ontario Power Generation — Maintenance Strategies Coop May 2021 - Dec 2021

Automated maintenance reporting with VBA and SSMS; built Power BI models and a Power App for station records.

Skills

Programming Languages

C++ C Java PHP HTML JavaScript CSS Python PowerShell Bash

Data & Analysis

SQL

Machine Learning / AI

Pandas NumPy Scikit-Learn SciPy

Cloud & Virtualization

VMware

Tooling

Git SQL Power BI

Projects

Smart Medicine Cabinet

Smart Medicine Cabinet — Capstone Project

The Smart Medicine Cabinet automates medication management using a Raspberry Pi, touchscreen, mechanical dispensers, and sensors. Built on the MagicMirror framework, it provides reminders, controlled dispensing, and remote caregiver monitoring to improve adherence and independence.

  • Roles: Designed the Smart Medicine Cabinet’s user interface using the MagicMirror Framework to deliver an intuitive, patient-friendly experience. Integrated the interface with the caretaker portal for real-time updates and remote oversight, and developed a Python-based gesture recognition script to verify medication intake—improving adherence, safety, and user independence.
  • Tech: Raspberry Pi, MagicMirror framework, IoT sensors, cloud sync
  • Awards: 1st Place — Ontario Tech Capstone Exhibition

Video · Code (GitHub) · Poster

Obstacle Detection

Obstacle Detection

This project demonstrates a basic obstacle detection system using computer vision techniques in Python with OpenCV. The system processes video input, detects vehicles using a pre-trained Haar Cascade classifier, and highlights obstacles in real time for visual analysis.

  • Roles: Developed a real-time obstacle detection pipeline by processing video frames, converting them to grayscale for optimized detection, integrating Haar Cascade classifiers for vehicle recognition, and implementing bounding box visualization for detected obstacles. Measured system performance and optimized frame processing efficiency.
  • Tech: Python, OpenCV, Haar Cascade Classifier, Computer Vision
  • Focus: Real-time video processing, object detection, performance measurement

Code (GitHub)

Contact

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