profile = {
    "name":       "Ibrahim Al-Shawa",
    "role":       "AI Engineer",
    "focus":      ["LLM Systems", "ML / Deep Learning", "Prompt Engineering"],
    "writing":    "Technical Writer — system design docs, SOPs, prompt specifications",
    "leadership": "Technical Head @ Enactus Port Said  (Aug 2025 – present)",
    "education":  "B.Eng. — Computer & Control Engineering, Port Said University, Egypt",
    "research":   "PBTune — Population-Based Training for DB parameter tuning  [open-source]",
    "interests":  ["Agentic AI", "Production ML Systems", "AI Research", "Software Architecture"],
}

I sit at the intersection of language, intelligence, and systems — engineering AI solutions end-to-end, from prompt design to deployed pipelines, while writing the documentation that makes them maintainable.

The first application of Population-Based Training (DeepMind's evolutionary hyperparameter-optimization algorithm) to automatic database parameter tuning. PBTune maintains a population of configurations that evolve online — poor performers exploit elite configs and explore nearby variations — discovering high-performance database settings without a DBA and without any pre-training phase.

Scores competitive performance metrics (latency / throughput), in 5x the wall-clock seconds of SOTA tuning tools.

Python · PostgreSQL · Docker · GCP Compute Engine · Evolutionary Optimization · HPO · Research

→ View Repository · → Documentation

A turnkey Software Development Standard Operating Procedure for driving agentic AI development from a Product Requirements Document (PRD) all the way to production deployment — with no gaps. Leverages a curated stack of prompting techniques.

Built to be the definitive guide for anyone using AI IDEs or agents to ship real software.

Prompt Chaining · ReAct · Contextual Priming · Negative Prompting · Tree of Thought · Self-Consistency

→ View Repository

A living "documenting-as-learning" reference — a structured guide covering ML and Deep Learning from first principles to expert-level topics, with practical coding examples and clear learning paths organized by interest area.

If you're learning ML/DL and want a single well-organized, practitioner-first resource — this is for you.

Machine Learning · Deep Learning · Jupyter · Python · Educational

→ View Repository

A multi-modal ML system for predicting cancer-related diseases using:

• Tabular data pipeline — classical ML with feature engineering & ensemble methods
• Image recognition pipeline — CNNs for histopathological image classification

scikit-learn · TensorFlow / Keras · Pandas · NumPy

→ View Repository

A feature-complete desktop audio player built using the Qt framework in Python, developed before AI-assisted development became mainstream — written by hand, every line.

Python · PyQt · Qt Framework · Desktop App

→ View Repository

Technical Committee Head — Enactus Port Said · Aug 2025 – Present

Enactus is a global non-profit operating in 36 countries, uniting student, academic, and business leaders to develop social entrepreneurship projects with real-world impact. As Technical Committee Head of the Port Said branch, I oversee the technical strategy, tooling, and execution of the committee's engineering and AI initiatives.