Generative AI Engineer with less than a year in LLMs & RAG
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Assessing your cultural and operational fit
Final-year Master's student in IoT & Big Data at INPT, with hands-on experience in Generative AI, LLM integration, RAG pipelines, prompt engineering and AI agents. Skilled in Python, APIs, FastAPI basics, Docker, embeddings, vector search, machine learning and data analytics. Experienced in building AI-driven systems combining LLMs, retrieval-based generation and Graph Neural Networks for intelligent decision support and cybersecurity use cases. Motivated to design practical GenAI applications, chatbots and LLM-powered tools that solve real business problems.
INPT National Institute of Posts and Telecommunications
Master's in IoT & Big Data
August 1, 2024 – Present
Mohammed V University - Faculty of Sciences
Bachelor's in Physical Sciences · Computer Science, Electronics and Automation
August 1, 2021 – June 30, 2024
INRS Institut National de la Recherche Scientifique
Research Intern - Intelligent IDS Agent with LLM + GNN
February 1, 2026 – Present
Canada
ONEE Water Branch
Automation and Supervision Intern
January 1, 2024 – February 1, 2024
Rabat, Morocco
Digital Payment Transactions Analytics Dashboard
January 1, 2026 – June 1, 2026
Analyzed online payment transaction data to identify patterns in transaction volume, amounts, fraud rates and risky transaction types. Built an interactive Power BI dashboard with KPIs, filters and DAX measures to support fraud monitoring and business reporting. Cleaned and transformed data using Python, Pandas, Excel and Power Query to ensure reliable and consistent reporting. Delivered business-oriented insights on transaction behavior and fraud patterns for data-driven decision-making.
Botola Pro Analytics - Final Top 3 Prediction
January 1, 2026 – June 1, 2026
Built an end-to-end predictive analytics pipeline to support sports performance forecasting and decision-making. Scraped historical match data from 2010 to 2026 with Selenium, including results, teams, matchdays, goals and home/away status. Engineered predictive features such as team form, points, goal difference, home/away performance, standings and Elo ratings. Trained ML models including Logistic Regression, Random Forest, XGBoost, LightGBM and CatBoost, and ran an Elo+Poisson simulation with 10,000 Monte Carlo runs to estimate title, Top 2 and Top 3 probabilities. Technologies: Python, Selenium, Pandas, Scikit-learn, XGBoost, CatBoost, Poisson regression.
AI Agent Architecture 365 Data Science
365 Data Science
June 1, 2026 – Present
Oracle Certified Professional OCI 2025 Data Science
Oracle
January 1, 2025 – Present
The candidate scored 88% on the Data Scientist — Artificial Intelligence test, indicating a solid grasp of the subject matter and strong problem-solving abilities within this domain.
Strengths
Limitations
Cultural Fit Analysis
The candidate's profile shows a strong inclination towards cutting-edge AI technologies, particularly Generative AI, LLMs, and AI Agents, which aligns well with a forward-thinking, innovation-driven culture. The involvement in a research internship and personal projects demonstrates initiative and a passion for applying AI to solve real-world problems. The breadth of skills from traditional ML to deep learning and cloud concepts indicates a versatile and continuous learner, fitting into a dynamic technical environment. The educational background in both Computer Science and IoT & Big Data provides a multidisciplinary perspective.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a methodical approach to problem-solving, from data scraping and feature engineering to model training and evaluation. The research intern role suggests an ability to work in an R&D environment, focusing on innovative solutions and detailed analysis. The clear articulation of project steps implies good communication of technical processes. The diverse project types (personal, professional, research) suggest adaptability and a proactive learning attitude.