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AI Engineer with less than a year in Data Science & Machine Learning
AI engineering student with a solid foundation in data science, machine learning, and deep learning, applied to natural language processing (NLP) and computer vision. Possessing hands-on experience in NLP, RAG, generative AI, and multi-agent systems, capable of designing and delivering end-to-end AI solutions using Python, Flask/FastAPI, LangGraph, PyTorch/TensorFlow for real-world use cases in legal and healthcare domains. Currently seeking a 2-3 month internship from July 2026 to contribute to innovative AI projects.
National School of Artificial Intelligence and Digital Technology (ENIAD)
Engineering Degree · Artificial Intelligence
August 1, 2024 – Present
Higher School of Technology (EST)
University Diploma of Technology (DUT) · Web & Mobile Application Development
August 1, 2022 – June 30, 2024
Farkhana High School
Baccalaureate · Physical Sciences
June 1, 2021 – May 31, 2022
Bagdys
Development of an e-business web application
May 1, 2024 – June 1, 2024
Nador, Oriental, Morocco
Medeva Solutions
Web Developer Intern
April 1, 2024 – October 1, 2024
Nador, Oriental, Morocco
Intelligent Legal Assistant
June 1, 2026 – Present
B2B legal-assistance web platform (RAG): scraping, PDF OCR, NLP and semantic search (FAISS) to search and generate legal content, with automatic case-file generation (Python, Flask). KPIs: search across 7,000+ rulings and 100+ legal texts in under 3 s; case-file drafting reduced by about 60%.
Multi-Agent Clinical Orientation Workflow
June 1, 2026 – Present
Multi-agent system (LangGraph) simulating a clinical pathway with 3 specialized agents, Human-in-the-Loop and PDF reporting, powered by a local LLM (Qwen) deployed via Docker (FastAPI, MCP, Streamlit). KPIs: complete report in under 15 s; pathway completion rate above 95%.
Object Detection (mobile)
June 1, 2026 – Present
Real-time object-detection CNN integrated into a Flutter app (TensorFlow, OpenCV). KPIs: ~20 FPS on mobile; mAP around 88%.
Facial Recognition
June 1, 2026 – Present
Real-time biometric access-control system (Deep Learning, Python, OpenCV). KPIs: accuracy around 96%; under 1 s per face.
Waste Classification (Computer Vision)
June 1, 2026 – Present
CNN model (PyTorch) for recyclable-waste classification, exposed via a FastAPI API. KPI: 90-95% accuracy on the test set.
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest in diverse AI applications, from legal tech to healthcare and environmental classification. This breadth of interest, combined with a focus on practical, real-world problem-solving, suggests a good cultural fit for an innovative AI engineering team. The internships, though not directly AI-focused, show exposure to professional development environments.
Soft Skills & Operational Fit
The candidate lists analytical thinking, problem-solving, teamwork, and continuous learning as soft skills. These are highly relevant for an AI Engineer role, indicating a proactive and collaborative mindset. The project descriptions show an ability to work on complex, multi-faceted problems and deliver measurable results (KPIs).