Available for AI / ML internships

Adem Guedri

AI Developer·Agentic AI · Multi-Agent · Automation

Computer Engineering student at UQU, Top 5% in the KAUST AI Program and IBM AI Lab trained. I build agentic AI systems, multi-agent pipelines, and computer vision applications with Python and PyTorch.

$build → train → deploy → repeat

Top 5% KAUST AI Program
IBM AI Lab
GPA 3.99 / 4.00

View projects View CV

01 / about

About me

Computer Engineering student at Umm Al-Qura University, ranked Top 5% in the KAUST AI Program with a 3.99 GPA. I focus on Artificial Intelligence, Computer Vision, and intelligent systems, building AI-driven solutions with Python, PyTorch, and Laravel that aim for performance, reliability, and real-world impact.

Through the IBM AI Lab I built a multi-agent healthcare platform, working on agentic AI, LLM orchestration, and production AI workflows. I continuously improve through hands-on projects and team collaboration, aiming to develop intelligent software that solves complex challenges.

focus areas

Agentic AI & multi-agent systems
Computer vision & deep learning
LLM applications & RAG pipelines
Shipping ML models into production

02 / experience

Experience

IBM

AI Developer Trainee

IBM · IBM AI Lab

Apr 2026 – Jun 2026 · 3 mosRemote

Completed a 10-week AI Industry Immersion program under IBM experts.
Built a multi-agent AI healthcare platform on IBM's enterprise AI stack, with specialized agents for medical-information summarization, treatment-plan tracking, and patient support.
Designed the LLM orchestration layer connecting multiple AI agents into a unified production pipeline.
Applied Agentic AI, Multi-Agent Systems, RAG, Agile, and Design Thinking in a collaborative team environment.
Presented the final solution to IBM evaluators and industry mentors.

AI Agents
Multi-Agent Systems
RAG
LLM Orchestration
Agile
Design Thinking

03 / projects

Projects

Aafiyah

HEALTHCARE AI / MULTI-AGENT

Code

A full-stack multi-agent AI healthcare platform built at the IBM AI Lab. AI agents convert clinical notes into structured patient care plans and medication schedules; doctors get a timestamped visit history for every patient.

Node.js
Express
Google Gemini
JWT
JavaScript

Content Inspiration

AI AUTOMATION / LOCAL LLM

Code

Streamlit application that scrapes articles from the Google AI Blog and processes them through a 4-step pipeline: link collection, content download, image retrieval, and AI summarization. Uses a local Ollama LLM for fully on-device inference with no external API calls. Includes YAML config, advanced search and filtering, and a content analytics dashboard.

Python
Streamlit
Ollama
LLM
Web Scraping

Trend Web

SOCIAL MEDIA / FULL-STACK

Code

Full-stack social media platform built with Laravel 11 and PHP 8.2. Features user registration with avatar upload, tweet/reply/retweet/like system, follow network, media gallery, and profile customization with banners and bios. Dark/light theme with responsive design using Blade, Tailwind CSS, and DaisyUI.

Laravel
PHP
Tailwind CSS
MySQL

See more on GitHub

04 / skills

Skills & tools

AI & Agents

Multi-Agent Systems
AI Agents
Agentic AI
RAG
LLMs

ML & Data Science

PyTorch
scikit-learn
Deep Learning
Computer Vision
Pandas
NumPy

Languages & Web

Python
PHP
Java
Laravel
Tailwind CSS

Data & Scraping

BeautifulSoup
Selenium
Requests

Databases & Networking

MySQL
SQLite
Cisco DevNet

05 / education

Education

Umm Al-Qura University

Bachelor's degree, Computer & Network Engineering

Aug 2024 – 2029

GPA: 3.99 / 4.00

Studying computer and network engineering: systems architecture, communication protocols, algorithms, and engineering fundamentals. Active in the Computer Club as Project Management Committee Member since February 2026, leading technical project coordination and cross-team collaboration.

Computer Architecture
Algorithms
Networking
Data Structures
Operating Systems
Leadership
Project Management

KAUST Academy

Artificial Intelligence Program, Top 5% of 12,000+ participants

Jan 2026 – Feb 2026

Selective AI program at KAUST Academy with over 12,000 participants, ranking in the top 5%. Introduction stage (with Distinction): Machine Learning Foundations, ML Algorithms, Deep Learning, and Unsupervised Learning. Advanced stage: Computer Vision, Datasets, Transfer Learning, Image Segmentation, and Object Detection.