A Computer Science graduate passionate about building production-grade AI pipelines and optimizing the software-hardware infrastructure beneath them. I don't just train models; I bridge the gap between high-level Deep Learning architectures and low-level system efficiency—focusing on concurrency, VRAM optimization, and seamless backend deployments.

• Systems Architecture: Concurrency & Asynchronous Pipelines, Multi-GPU/CPU Resource Allocation, Containerization (Docker/Docker Compose), Low-Level Binary Data Parsing.
• AI/ML Ecosystem: Audio Signal Processing, Deep Learning Pipelines, Speech & Language Models (ASR/SER/NLP), PyTorch, TensorFlow, Keras, Hugging Face (Whisper, Wav2Vec2), Qwen LLMs, Scikit-Learn, Librosa, SoundFile.

An enterprise-ready AI backend microservice engineered to power a therapist-patient mental health platform through automated, high-fidelity vocal analysis.

• The Architecture: Built a high-performance RESTful API via FastAPI and Uvicorn designed to process large, compute-heavy clinical audio recordings asynchronously.
• Concurrency & Stability: Implemented asynchronous task queues using FastAPI BackgroundTasks to ingest and process files sequentially, eliminating server-side timeouts and managing high-concurrency requests safely.
• Memory Optimization: Designed an in-memory dynamic audio-slicing pipeline using Librosa and SoundFile to stream continuous text chunks, drastically lowering the peak VRAM footprint on execution hardware.
• Multi-Model Ingestion Pipeline: Deployed an offline Egyptian-Arabic Whisper model (egyptian-whisper-v3) running on parallel GPU streams, integrated alongside a Wav2Vec2 architecture for emotion detection and a CAMeLBERT sentiment pipeline. Structured clinical summaries are generated via a localized Qwen LLM configured with token suppression.
• Deployment: Containerized the entire infrastructure using Docker/Docker Compose and established secure Ngrok tunnels for frictionless integration testing with the client team.

A core computer science deep-dive project mapping high-level vision logic down to custom low-level algorithmic implementations.

• OCR Pipeline Engineering: Engineered an Optical Character Recognition (OCR) application from scratch to classify handwritten digits (0-9) using foundational Machine Learning classification algorithms.
• Signal to Feature: Authored the entire data lifecycle including image preprocessing, digital noise reduction, and a manual pixel-grid feature extraction system to maximize model training precision.
• Low-Level Binary Parsing: Refactored a complex heat diffusion simulation matrix, stripping out random data generation in favor of a custom parser that reads and ingests raw structured matrix configurations directly from local binary storage files (.bin).

A high-throughput text data engineering project designed to scrape, clean, index, and query unstructured web data efficiently.

• Data Pipeline: Developed a resilient web scraping network utilizing BeautifulSoup and Scrapy to ingest, parse, and clean massive amounts of unstructured data from targeted live domains.
• Search Architecture: Architected a lightweight custom search engine from the ground up, implementing index tokenization, text-based algorithmic filtering, and analytical ranking metrics.
• Backend Engineering: Wired the processing layer into a clean Python backend, ensuring sub-second query execution times and optimized data routing from storage to the client interface.

• Email: [email protected]
• LinkedIn: linkedin.com/in/ahmed-magdy-50808a250
• GitHub: github.com/Sober-Migo