Riadh's Personal Site

Principal Data Scientist Industrial AI • CV • NLP

Riadh Belkebir

Ph.D. in Artificial Intelligence | Data Science | Computer Vision | NLP

Professional Summary

Riadh Belkebir is a Principal Data Scientist holding a Ph.D. in Artificial Intelligence, with more than a decade of experience spanning academic research, applied industrial AI, and enterprise-scale data science platforms. His expertise covers Machine Learning, Deep Learning, Computer Vision, and Natural Language Processing, with a strong emphasis on deploying AI systems in real-world, safety-critical, and large-scale industrial environments.

He has led the design and implementation of real-time video analytics platforms, computer vision inference pipelines, and cloud-native MLOps architectures using Kubernetes, Docker, and Azure AKS. His work bridges IT and OT domains, integrating AI solutions with PLC, MES, and industrial control systems. In parallel, he maintains a strong academic profile with peer-reviewed publications in high-impact journals including Expert Systems with Applications and ACM Transactions on Asian and Low-Resource Language Information Processing, as well as multiple book chapters and international conference contributions.

His background combines deep theoretical foundations with hands-on leadership, mentoring teams, supervising students, and driving AI initiatives from research through production and enterprise adoption.

Professional Experience
Principal Data Scientist — Emirates Global Aluminium (EGA)
Feb 2025 – Present
  • Lead enterprise-scale AI platforms focused on industrial safety, compliance, monitoring, and advanced analytics across multiple production sites.
  • Define reference architectures for real-time computer vision inference, including camera ingestion, GPU-based inference services, event processing, and alerting pipelines.
  • Design scalable MLOps frameworks covering model training, validation, deployment, monitoring, retraining, and governance.
  • Drive integration between AI systems and industrial environments, including PLC and MES systems, enabling automated safety responses.
  • Collaborate with IT, OT, cybersecurity, and business stakeholders to align AI solutions with operational and regulatory requirements.
  • Mentor senior and junior data scientists and contribute to long-term AI strategy and roadmap definition.
Lead Data Scientist — Emirates Global Aluminium (EGA)
Jul 2024 – Feb 2025
  • Led flagship AI programs such as Smart Cranes and Safety Rodding AI, covering end-to-end lifecycle from problem definition to production rollout.
  • Designed and deployed real-time video analytics pipelines using YOLO-based models, OpenCV, and GPU acceleration.
  • Architected microservice-based inference systems deployed on Kubernetes, supporting scalability and fault tolerance.
  • Established best practices for data labeling, model evaluation, performance monitoring, and production readiness.
  • Provided technical leadership and mentoring to data science teams.
Data Scientist — Emirates Global Aluminium (EGA)
Feb 2023 – Jul 2024
  • Developed and deployed production-grade computer vision pipelines for harsh industrial environments.
  • Implemented real-time inference systems integrated with enterprise data platforms and control systems.
  • Conducted model optimization, performance benchmarking, and error analysis for large-scale deployment.
Assistant Professor — Amity University Dubai
Sep 2021 – Feb 2023
  • Teach undergraduate and postgraduate courses in Artificial Intelligence, Machine Learning, and Data Analysis.
  • Supervise undergraduate capstone projects in applied AI and data science.
  • Mentor students in research methods, applied machine learning, and industrial problem-solving.
Postdoctoral Associate — New York University Abu Dhabi
Sep 2020 – Sep 2021
  • Conducted research in explainable AI, focusing on error analysis and justification in NLP systems.
  • Worked on Arabic spelling correction and OCR post-processing pipelines.
  • Co-supervised undergraduate research projects and contributed to shared research infrastructure.
Senior Data Scientist – NLP — SAAL.AI (UAE)
Mar 2019 – May 2020
  • Designed and deployed Arabic NLP systems including semantic search, sentiment analysis, NER, and topic modeling.
  • Led development of an Arabic healthcare chatbot (version 2), covering intent classification and dialogue management.
  • Built predictive typing, spelling correction, and market research analytics solutions.
Data Scientist – NLP — SAAL.AI (UAE)
May 2018 – Mar 2019
  • Developed Arabic text correction and sentiment analysis systems.
  • Contributed to early versions of Arabic healthcare chatbot platforms.
  • Performed data analysis, feature engineering, and predictive modeling.
Data Scientist — Self-employed
Jan 2018 – May 2018
  • Initiated and designed a data acquisition and analytics platform for agriculture-focused applications.
Research Assistant — USTHB University, Algeria
Dec 2016 – Dec 2017

Worked on a national research project (CNEPRU) focused on the development of an Arabic NLP toolbox.

  • Development of an Arabic abstractive text summarization module.
  • Development of an Arabic text categorization module.
Technologies: Python, Pandas, NumPy, spaCy, NLTK, Scikit-learn, Stanford CoreNLP, nntool, LibSVM, Weka, Flask.
Software Engineer — ELIT SONELGAZ, Algeria
Nov 2014 – Nov 2016

Java EE software engineer on enterprise applications: full SDLC from design to deployment and user support.

  • Design and development of a business management application.
  • Design and development of a billing application.
  • Design and implementation of an Enterprise Service Bus (ESB) to enable integration between ERP modules.
  • Development of a reporting solution using JasperServer exposed via REST APIs.
  • Training and support of end users on deployed systems.
Technologies: Java EE, Web Services, REST APIs, PrimeFaces, HTML, CSS, Retrofit, JasperServer, GlassFish, PostgreSQL.
Computer Programming Teacher — USTHB University, Algeria
Sep 2012 – Aug 2014
  • Delivered lectures and labs on programming fundamentals and problem-solving.
  • Taught algorithms, data structures, and structured programming.
  • Prepared course materials, exercises, assignments, and examinations.
  • Assisted students in developing correct, readable, and well-structured code.
Education
Ph.D. in Artificial Intelligence — USTHB University, Algeria
2017
Thesis: Arabic Text Summarization by Abstraction
Evaluation: Very Honourable Distinction
Advisor: Prof. Ahmed Guessoum
M.Sc. in Artificial Intelligence — USTHB University, Algeria
2011
Thesis: Automatic Categorization of Arabic Text
Advisor: Prof. Ahmed Guessoum
B.Sc. in Computer Science — USTHB University, Algeria
2009
Publications & Contributions
Refereed Journal Publications
  1. Hadj Ameur, M. S., Belkebir, R., & Guessoum, A. (2020). Robust Arabic Text Categorization by Combining Convolutional and Recurrent Neural Networks. ACM Transactions on Asian and Low-Resource Language Information Processing.
  2. Djenouri, Y., Belhadi, A., & Belkebir, R. (2018). Bees Swarm Optimization Guided by Data Mining Techniques for Document Information Retrieval. Expert Systems with Applications, 94, 126–136.
  3. Belkebir, R., & Guessoum, A. (2016). Concept Generalization and Fusion for Abstractive Sentence Generation. Expert Systems with Applications, 53, 43–56.
Book Chapters
  1. Belkebir, R., & Guessoum, A. (2018). TALAA-ATSF: A Global Operation-Based Arabic Text Summarization Framework. Springer, Cham.
  2. Belkebir, R., & Guessoum, A. (2015). A Supervised Approach to Arabic Text Summarization Using AdaBoost. Springer, Cham.
International Conferences
  1. Belkebir, R., & Habash, N. (2021). Automatic Error Type Annotation for Arabic. ACL / CoNLL-EMNLP.
  2. Belkebir, R., & Guessoum, A. (2015). TALAA-ASC: A Sentence Compression Corpus for Arabic. IEEE AICCSA.
  3. Belkebir, R., & Guessoum, A. (2013). Hybrid BSO-Chi2-SVM Approach to Arabic Text Categorization. IEEE AICCSA.
National Conferences
  1. Belkebir, R., & Guessoum, A. (2014). AdaBoost-based Approach to Arabic Text Summarization. JEESI’14.
  2. Belkebir, R. (2013). Voting-Based Model for Arabic Text Summarization. National Doctoral Conference, Skikda.
  3. Belkebir, R., & Guessoum, A. (2012). Hybrid Approach to Arabic Text Categorization. USTHB.
Academic Service

Reviewer for international journals and conferences including: EACL 2021, WANLP 2021, ACM TALLIP, ICNLSP 2018, ISPS 2018, CICLing 2016. Organizing Committee Member for Artificial Intelligence Doctorials (2012, 2014) and ISPS 2013.

Core Technical Skills
Programming Languages
Python Java (Java EE) C / C++ MATLAB PHP
Machine Learning & AI
Supervised / Unsupervised Deep Learning XAI Time Series Anomaly Detection Benchmarking
Computer Vision
Detection Segmentation Tracking Video Analytics YOLO OpenCV
NLP
Arabic NLP Summarization NER Sentiment Topic Modeling OCR Post-processing
MLOps & Platforms
Kubernetes (AKS) Docker Helm MLflow Databricks Redis CI/CD
Training & Professional Development
Machine Learning, MLOps & AI Platforms
  • Coursera Machine Learning Certificate — Andrew Ng, Stanford University (2014)
  • MLOps Essentials: Model Development and Integration — LinkedIn Learning, Kumaran Ponnambalam (Updated May 2025)
  • Complete Guide to Python Fundamentals for MLOps — LinkedIn Learning, Alfredo Deza & Pragmatic AI Labs (Sep 2024)
  • Azure Kubernetes Service (AKS): Deploying Microservices — LinkedIn Learning, Prince Mokut (Oct 2022)
  • Azure Spark Databricks Essential Training — LinkedIn Learning, Lynn Langit (Updated Feb 2025)
  • Azure: Understanding the Big Picture — LinkedIn Learning, Walt Ritscher (2022)
  • Microsoft Azure Fundamentals (AZ-900) – Cloud Concepts — LinkedIn Learning, Kunal D. Mehta
Generative AI & Prompt Engineering
  • Complete Guide to Generative AI for Data Analysis and Data Science — LinkedIn Learning, Dan Sullivan (Sep 2024)
  • Introduction to Prompt Engineering for Generative AI — LinkedIn Learning, Ronnie Sheer (Aug 2024)
  • Prompt Engineering: How to Talk to the AIs — LinkedIn Learning, Xavier Amatriain (Apr 2023)
  • Discover the Possibilities of Generative AI — LinkedIn Learning, Ashley Kennedy (Updated Apr 2025)
  • Introducing Semantic Kernel: Building AI-Based Applications — LinkedIn Learning, John Maeda & Sam Schillace (Mar 2023)
Responsible AI, Governance & Compliance
  • AI Show: Deep Dive into Responsible AI Dashboard and Scorecard — Microsoft Learn / LinkedIn Learning, Seth Juarez (Mar 2023)
  • Learning GDPR — LinkedIn Learning, Kalinda Raina (Updated May 2022)
Leadership, Strategy & Digital Transformation
  • AI Challenges and Opportunities for Leadership — LinkedIn Learning, Conor Grennan (Oct 2023)
  • How to Keep Your Team on the Bleeding Edge of AI Innovation — LinkedIn Learning, Aishwarya Srinivasan (May 2024)
  • Leading at a Distance — LinkedIn Learning, Kevin Eikenberry (2019)
  • Digital Transformation — LinkedIn Learning, Peter High (2018)
  • Learning Data Science: Manage Your Team — LinkedIn Learning, Doug Rose (2020)
Data, Software Engineering & Collaboration
  • Advanced SQL for Data Scientists — LinkedIn Learning (2020)
  • Complete Guide to Tableau for Data Scientists — LinkedIn Learning, Matt Francis (Sep 2024)
  • Learning SOLID Programming Principles — LinkedIn Learning, Steven Lott (Mar 2022)
  • Git from Scratch — LinkedIn Learning, Morten Rand-Hendriksen (Jun 2022)
  • What Is Scrum? — LinkedIn Learning, Kelley O’Connell (Apr 2020)
  • Data Analytics for Business Professionals — LinkedIn Learning, John Johnson (2022)
  • Nano Tips to Enhance Your Communication — LinkedIn Learning, Shadé Zahrai (Jan 2023)
  • Java EE Profiling and UML Advanced Concepts — ELIT SONELGAZ
Languages
Arabic — Native English — Professional French — Professional
© 2026 Riadh Belkebir — Blog-style CV