People

Abdul Aziz Ahmed

Admin & Technology Lead
Riyadh+966 114944999

Abdul Aziz is a Software Engineer specializing in artificial intelligence, robotics, and full-lifecycle software development. Holding a Bachelor of Technology in Computer Science Engineering with a specialization in AI and Robotics. He graduated blending rigorous academic foundations with practical industry expertise. Currently based in Riyadh, Saudi Arabia, Abdul brings a deep understanding of intelligent technology solutions and scalable system architecture to the leadership team, spearheading the development of AI-based tools and ensuring enterprise infrastructures remain highly available and secure.

Full Bio

With a robust professional background that includes significant engineering tenure in Seoul, South Korea, Abdul has played a pivotal role in building advanced database extensions and modifying custom Large Language Models (LLMs). He expertly manages Linux production servers, designs robust network security protocols, and drives operational excellence by automating workflows and utilizing platforms like Cloudflare and Grafana to monitor and optimize system reliability. His technical acumen is further supported by numerous professional certifications across major cloud computing providers, demonstrating his proficiency in leveraging modern AI frameworks to build resilient backend systems.

Credentials

Education
  • Bachelor of Computer Science Engineering, VIT University
Certifications
  • Microsoft Certified: Azure AI Fundamentals AI-900
  • Microsoft Certified: Azure Data Fundamentals DP-900
  • Microsoft Certified: Azure Fundamentals AZ-900
  • Oracle Cloud Infrastructure Certified Generative AI Professional
Languages
  • English
  • Spanish

Publications

DecentraliDrone: A decentralized, fully autonomous drone delivery system for reliable, efficient transport of goods

Elsevier — Alexandria Engineering Journal. The system ensures faster, accurate deliveries by eliminating human-operated constraints. Utilizes advanced algorithms for real-time danger detection and collaborative decision-making.

A fusion approach using GIS, green area detection, weather API and GPT for satellite image based fertile land discovery and crop suitability

Springer — Nature Scientific Reports. Created a self-learning nano-GPT architecture based on Mistral-7B and finetuned it to give crop suggestions for any given land on the world map, taking into account the geospatial attributes.