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Saudi Arabia

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AI Engineer | LLMs, Agentic AI, RAG Pipelines, MLOps & Scalable ML Systems

Profile Summary

AI Engineer and Researcher with 3+ years of experience building production-ready machine learning and Large Language Model (LLM) systems. My work focuses on LLM evaluation, Retrieval-Augmented Generation (RAG), agentic AI systems, and scalable MLOps pipelines on cloud platforms. I have designed and deployed end-to-end ML systems for real-world applications, including cybersecurity threat detection and AI-driven automation. My expertise includes building agent-based LLM workflows, integrating external APIs, and developing reproducible ML pipelines with CI/CD. I am also an active researcher with multiple peer-reviewed publications in top journals (IEEE Access, Computers), focusing on LLM evaluation, AI-driven code documentation, and intelligent systems. My technical stack includes Python, PyTorch, TensorFlow, LangChain, LlamaIndex, vector databases, and cloud platforms such as AWS and Microsoft Azure. I am passionate about applying cutting-edge AI research to solve real-world problems and collaborating on innovative projects in AI, machine learning, and intelligent automation.

Subject Matter Expertise

• ☆ Computer Science
• ☆ Python
• ☆ SQL
• ☆ Tensorflow
• ☆ LaTex
• ☆ Scikit-learn
• ☆ Oracle
• ☆ Hadoop
• ☆ Peer Review
• ☆ Research Methodology
• ☆ Survey Design & Methodology
• ☆ User Research
• ☆ Quantitative User Research
• ☆ Qualitative User Research
• ☆ Gap Analysis
• ☆ Gray Literature Search
• ☆ Literature Review
• ☆ Systematic Literature Review

Services

• Research
• Data & AI

Research Technology Scouting, Gap Analysis, Systematic Literature Review

Data & AI Predictive Modeling, Statistical Analysis, Image Analysis, Algorithm Design-Non ML, Data Visualization, Big Data Analytics, Data Mining, Data Cleaning, Data Processing

Work Experience

King Fahd University of Petroleum and Minerals

- Present

Machine Learning & MLOps Engineer

DefencyLab

June 2023 - June 2025

Education

Master of Science in Computer Science (Artificial Intelligence)

King Fahd University of Petroleum and Minerals

January 2024 - Present

focus: AI systems, LLMs, and applied machine learning

ENIS

September 2020 - June 2023

Certifications

• MTA: Introduction to Programming Using Python

  Microsoft

  https://drive.google.com/file/d/17NXcClv52qMd67Bb163Gpbiiy4LeYgjG/view

  January 2026 - Present

• Atlassian Certified in Managing Jira Projects for Cloud

  Atlassian

  https://cp.certmetrics.com/atlassian/en/public/verify/credential/cd946daa42914141836a691cd759fcdd

  October 2025 - Present

• GitLab Certified Git Associate

  Gitlab

  https://www.credly.com/badges/9e7b9103-050b-4f98-85dd-64919f22b8fa/print

  June 2024 - Present

• Microsoft Azure Fundamentals (AZ-900)

  Azure

  https://drive.google.com/file/d/1EUrBf-tcynOibRZLtA3TGCYv5wK1oVY4/view

  January 2023 - Present

Publications

JOURNAL ARTICLE

Mohamed Mehdi Trigui, Wasfi G. Al-Khatib, Mohammad Amro, Fatma Mallouli (2026). AI-Driven Code Documentation: Comparative Evaluation of LLMs for Commit Message Generation . Computers.

Mohamed Mehdi Trigui, Wasfi G. Al-Khatib, Mohammad Amro, Fatma Mallouli (2026). AI-Driven Code Documentation: Comparative Evaluation of LLMs for Commit Message Generation . Computers.

Mohamed Mehdi Trigui, Md Sakibul Islam, Muhammad Dikko Gambo, Mir Suhail Alam, Tarek Helmy (2026). Resource-Aware Ensemble Framework for DDoS Detection in SDN via Novel Game-Theoretic Feature Selection . IEEE Access.

Mohamed Mehdi Trigui, Wasfi G. Al-Khatib (2025). LLMs for Commit Messages: A Survey and an Agent-Based Evaluation Protocol on CommitBench . Computers.

Mohamed Mehdi Trigui, Wasfi G. Al-Khatib (2025). LLMs for Commit Messages: A Survey and an Agent-Based Evaluation Protocol on CommitBench . Computers.

Mohamed Mehdi Trigui, Wasfi G. Al-Khatib (2025). LLMs for Commit Messages: A Survey and an Agent-Based Evaluation Protocol on CommitBench . Computers.

PREPRINT

Mohamed Mehdi Trigui, Wasfi G. Al-Khatib, Mohammad Amro, Fatma Mallouli (2025). AI-Driven Code Documentation: Comparative Evaluation of LLMs for Commit Message Generation .

WORKING PAPER

AI-Driven Code Documentation: Comparative Evaluation of LLMs for Commit Message Generation .