Omar
Nahdi

Designed and deployed production-ready AI systems for an early-stage e-commerce startup, focusing on LLMs, RAG pipelines, and agent architectures. Reduced vector search latency by 50%, built end-to-end agent workflows on AWS (500+ users), and conducted technical screening for 100+ AI/ML candidates.

Built and evaluated classification and regression models on real-world datasets using Python and scikit-learn. Improved predictive performance through structured feature engineering, cross-validation, and targeted hyperparameter tuning. Designed reproducible experimentation workflows to systematically compare models and ensure reliable results.

Pursuing a focused curriculum in artificial intelligence and machine learning with emphasis on applied ML, deep learning, and data-driven system design, building strong foundations in model development, optimization, and real-world AI problem solving while actively working on practical projects alongside coursework.

Completed an intensive diploma program centered on applied machine learning, covering data preprocessing, model building, and evaluation using Python-based ML stacks, with hands-on experience translating theoretical concepts into working ML solutions through projects and practical assignments.