(Evolving from Laravel craftsman to Python backend engineer 🐍)

class EslamKamel:
    def __init__(self):
        self.role = "Backend Developer"
        self.specialties = ["Python", "Django", "FastAPI", "Laravel", "Docker"]
        self.learning_track = "AI/ML (slow but steady)"
        self.current_job = "Shipping APIs, automating things, and refactoring life"
        self.superpower = "Turning caffeine into clean code and working containers"
        self.motto = "If it runs, ship it. If it fails, containerize it."

    def say_hi(self):
        print("Hire me? (async of course)")

EslamKamel().say_hi()

I’m a backend developer fluent in both Laravel and Python (Django / FastAPI). Currently shifting my main stack toward Python for modern API development, but still actively delivering Laravel production systems — from APIs and queues to CI/CD and real-time features.

I love building systems that are:

• Clean: typed, tested, documented
• Performant: async-ready, containerized
• Reliable: optimized queries, stable queues, predictable deploys

In transition: learning how to blend Python backends + AI/ML to build smarter, data-driven platforms — one Jupyter cell at a time.

✔️ Laravel & Livewire apps with clean architecture, queues, caching, and multi-tenant setups

✔️ Django/DRF + FastAPI APIs — async, typed, tested, containerized

✔️ Docker setups so portable they could run on a toaster

✔️ CI/CD pipelines that keep production calm and predictable

Fun facts:

🐧 I talk to my containers when they misbehave

🐍 I think in Python, dream in PHP

☕ I treat caffeine as infrastructure

• FastAPI – async APIs, Pydantic models, OAuth2, SQLAlchemy
• Django + DRF – admin, ORM, Celery, Redis, PostgreSQL
• Pytest – testing with fixtures and coverage
• Docker + Docker Compose – full dev environments
• Gunicorn + Nginx – production serving

• Laravel 11.x – REST APIs, Sanctum, queues, caching, notifications
• Livewire / Volt / Filament – modern reactive interfaces
• Inertia.js / Vue / React – dynamic SPA frontends
• Stripe / PayPal / WebSockets – real-time and payments integration

• Flutter – published apps on Play Store & App Store
• Vue 3 / Nuxt.js / React – SPAs and admin dashboards
• Tailwind CSS / Bootstrap – responsive UI foundations

I’m currently pursuing a 2-year AI/ML learning plan (2024–2026) — ~6 months completed so far.

Focus areas:

• Machine Learning (Scikit-learn, Pandas, NumPy)
• NLP and RAG (Hugging Face, LangChain)
• Deployment (FastAPI, Docker, AWS)

🧠 Goal: evolve from “backend dev” to “AI-ready backend engineer.”

• GitHub Actions – test → build → deploy
• AWS (EC2, ECS, S3, Route53) – scalable deployment
• Postman / pytest / curl – API testing workflow
• Nginx / Redis / Docker Compose – production infrastructure

if you.like_my_code:
    print("Let’s build something awesome together!")
else:
    print("Feedback also welcome — I’ll log it and retry.")

Links: 🔗 Portfolio 💼 LinkedIn 💻 GitHub

Bonus: I also build and publish Flutter apps

P.S. Still refactoring my sleep schedule into async tasks.