Mohamad Ali Al Saidi | AI & Machine Learning Engineer

About Me

I am a PhD candidate in Artificial Intelligence and a dedicated Machine Learning Engineer. I possess deep expertise in designing, training, and validating sophisticated models across a diverse spectrum of AI domains—from computer vision and NLP to time-series forecasting, anomaly detection, recommendation systems, and reinforcement learning.

My work heavily focuses on efficient learning and deployability. I specialize in model architecture design, hyperparameter optimization, and end-to-end AI workflows. Whether it requires distributed training on GPU clusters or optimizing ultra-compact models for TinyML applications on edge devices, my goal is to achieve state-of-the-art results that meet strict production SLAs.

End-to-End AI Data, Training to Cloud & Edge

TinyML Expert Pruning, Quantization, Edge AI

Academic Rigor PhD & Research Background

Focus Areas

Deep Learning & ML

Designing and validating advanced models using PyTorch, TensorFlow. Expertise in hyperparameter optimization and continuous monitoring for robust performance.

TinyML & Edge AI

Applying model pruning, quantization, and Neural Architecture Search (NAS) to deploy ultra-compact models on embedded devices like Arduino Nano 33 BLE.

Computer Vision

Building real-time object detection and segmentation systems utilizing YOLOv8, Mask R-CNN, and Faster R-CNN deployed on diverse hardware profiles.

Multilingual NLP

Fine-tuning LLMs (BERT, GPT, T5) for text summarization, NER, question-answering, and knowledge graph extraction with robust pipeline implementation.

Education

2023 – Present

Ph.D. in Computer Engineering, Artificial Intelligence & Robotics

Ferdowsi University of Mashhad • Mashhad, Iran

Focusing on advanced AI research, combining deep learning methodologies with robust engineering practices for deployable intelligence.

2024 – 2026

Doctor of Business Administration (DBA)

University of Tehran • Tehran, Iran

2020 – 2022

M.Sc. in Computer Engineering, AI & Robotics

Ferdowsi University of Mashhad • Mashhad, Iran

GPA: 3.9/4.0

Thesis: Hierarchical Solving of Euclidean Geometry Ruler and Compass Construction Problems Using Visual Knowledge Representation and Curriculum Learning.

2022 – 2023

Master of Business Administration (MBA)

Ferdowsi University of Mashhad • Mashhad, Iran

2016 – 2020

B.Sc. in Computer Engineering, Artificial Intelligence

Ferdowsi University of Mashhad • Mashhad, Iran

GPA: 3.9/4.0

Professional Experience

2018 – 2025

Airline System Developer

Beirut, Lebanon

Architected and implemented a modular airline reservation and management platform supporting high-volume daily bookings.
Engineered an AI-driven OCR module for passport data extraction using CV models, achieving 95% accuracy and reducing manual processing time by 80%.
Developed interactive UI components and dashboards to visualize real-time booking metrics.
Designed robust ETL pipelines in Python and SQL, ensuring 99% data integrity.
Managed Windows servers/network infrastructure, enforcing security and disaster recovery for 99.9% uptime.

2017 – 2018

System Administrator Intern

ICT Department, Ferdowsi University of Mashhad • Mashhad, Iran

Maintained university ICT infrastructure: installed, configured, and troubleshot Windows/Linux servers, workstations, and network equipment.
Led deployment of security patches and system upgrades, reducing critical vulnerabilities by 90%.
Automated routine maintenance tasks with Bash and PowerShell scripts, cutting weekly manual effort by 50%.

Key Projects & Applied Research

LLMs & Natural Language Processing

Fine-tuned BERT, GPT, and T5 transformer models on specialized datasets for text summarization, QA, and classification.
Developed a Persian medical report summarization system (30% faster clinical info extraction).
Implemented NLP pipelines for NER, knowledge graph extraction, and document clustering.

Transformers Hugging Face Python

Computer Vision & Object Detection

Built real-time detection systems using YOLOv8, Mask R-CNN, and Faster R-CNN on GPUs and Edge devices.
Developed high-accuracy passport extraction pipeline utilizing YOLOv8.
Performed advanced image processing tasks (stitching, restoration, enhancement, fisheye correction).

YOLOv8 OpenCV PyTorch

TinyML & Embedded Systems

Applied model pruning, quantization, and NAS to develop ultra-compact DL models.
Successfully deployed optimized TinyML models on Arduino Nano 33 BLE, maintaining high inference accuracy with minimal power usage.

TensorFlow Lite Micro MCUNet Arduino

Reinforcement Learning Agents

Trained agents across Atari, CartPole, mazes, and custom shooters using advanced reward shaping and exploration.
Algorithms implemented include Q-Learning, SARSA, DQN, PPO, Actor-Critic, and MCTS.

PPO DQN Python

Robotics & Simulation

Programmed autonomous navigation and object manipulation using TurtleBot3 and robotic arms in ROS/Gazebo.
Integrated LiDAR sensors for real-time perception, SLAM-based localization, and obstacle avoidance.

ROS Gazebo C++

Software & Systems Engineering

Developed scalable web systems (car rental, recommenders) using Java, PHP, SQL.
Built secure communication modules (AES, RSA) and kernel-level Linux tools.
Engineered custom compiler and CPU architecture in Verilog.

Java/PHP Verilog Linux Kernel

Technical Skills

Programming & Core

Python Java C/C++ PHP SQL Prolog JavaScript

AI & Deep Learning

PyTorch TensorFlow Keras Scikit-learn Hugging Face MCUNet

TinyML & Edge AI

TensorFlow Lite Micro Arduino Nano 33 BLE Edge Impulse Model Pruning Quantization

Computer Vision

OpenCV YOLO (v5/v8) Mask R-CNN Faster R-CNN

Robotics & Tools

ROS Gazebo LiDAR Git Docker Linux LaTeX

Languages

Arabic (Native) English (C1) Persian (C2)

Get In Touch

Email

Eng.mohamad.ali.saidi@gmail.com

Phone

+961 3 013 113

Location

Beirut, Lebanon