Hello, World!

I'm Ahmed Eleawa

Software Engineer / ML Engineer — I build intelligent systems, fast backends, and low-level weird stuff.

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// 01. about

What I work on

01

AI R&D

I focus on artificial intelligence research and development, particularly deep learning and model behavior. I enjoy exploring how learning systems work, experimenting with ideas, and building intelligent systems that can generalize and adapt.

02

Low-Level & Systems

I’m interested in low-level programming, performance, and how software interacts with hardware. I work with languages like C and Rust, and I’m exploring areas such as memory management, concurrency, and GPU computation.

03

Software Engineering

I build backend systems and applications with a focus on clean architecture, scalability, and efficiency. I enjoy designing structured, maintainable systems and connecting high-level logic with practical implementations.

// 02. arsenal

Skills

Python SQL C C++ Rust CUDA Numpy Pandas Scikit-learn Pytorch TensorFlow LangChain Matplotlib Seaborn Redis HTML/CSS JavaScript Node.js React Next.js Tailwind CSS FastAPI Flask Django PostgreSQL Nginx

// 03. selected work

Projects with teeth

01

EUROSAT Land Type Classification

A deep learning-based image classification system built on the EuroSAT dataset to identify land-use categories from satellite imagery. The project applies convolutional neural networks to extract spatial features and classify different terrain types.

The system focuses on data preprocessing, model training, and evaluation, exploring how deep learning models learn representations from high-dimensional visual data and generalize across different land patterns.

Focus: Image classification, representation learning

Stack: Python, PyTorch, Computer Vision

PyTorchCNNsEuroSAT

View on GitHub

02

Wav2Lip + AnimateDiff Pipeline

A custom pipeline combining Wav2Lip and AnimateDiff to generate synchronized lip movements and animated video outputs from audio inputs. The system integrates speech-driven facial animation with generative diffusion models to produce temporally coherent and realistic results.

The pipeline focuses on aligning audio features with visual generation while maintaining consistency across frames, exploring the intersection of audio-visual synchronization and generative modeling techniques.

Focus: Audio-visual synchronization, generative video models

Stack: Python, PyTorch, Diffusion Models, Audio Processing

DiffusionWav2LipVideo Gen

View on GitHub

// 04. volunteering

Giving back

Instructor

Data Analysis & AI Instructor — GDG on Campus

Modern Academy in Maadi, Department of Computer Science

I teach data analysis and AI — from Python and data wrangling to machine learning fundamentals — helping students go from theory to hands-on projects.

TeachingData AnalysisMachine Learning

GDG Chapter  ·  Modern Academy

// 05. services

What I can do for you

01

ML Model Development

Custom models designed, trained, and evaluated around your data — from baselines to production-minded pipelines.

PyTorchFine-tuningEvaluation
02

Data Analysis & Pipelines

Exploratory analysis, cleaning, and ETL that turn raw data into decisions — with clear visuals and reports.

PandasETLDashboards
03

Backend Development

Fast, clean APIs and services with FastAPI or Django — backed by PostgreSQL, Redis, and Nginx.

FastAPIPostgreSQLNginx
04

AI Tutoring & Mentoring

1-on-1 or group sessions on Python, data analysis, and machine learning — theory tied to hands-on projects.

PythonML BasicsProjects
Request a service

// 06. contact

Let's build something sick.

Fast replies. No recruiter spam energy. If it's about AI, systems, or backends — I'm in.

eleawap@gmail.com