Walk me through your hands-on experience building Python backend applications, and how you decide between FastAPI, Flask, and Django.

In Simple Words: "I am a Senior Backend and Full-Stack Engineer specializing in high-performance Python, asynchronous microservices, and distributed architectures. I build services using FastAPI, Flask, and Django, integrated with async database drivers (asyncpg), message brokers (RabbitMQ, Redis), and background task workers (Celery, ARQ).

My framework decision matrix:

• FastAPI: My default choice for modern, high-throughput microservices. Built on Starlette and Pydantic v2, it features native AsyncIO ASGI concurrency, automatic OpenAPI/Swagger documentation, and compile-time type validation.
• Flask: Best for small utility microservices and lightweight automation scripts where an un-opinionated, minimal footprint is desired.
• Django: Best for full-featured monoliths requiring built-in administrative dashboards, auth systems, and robust ORM features out of the box."

# FastAPI modern async route with Pydantic v2 validation:
from fastapi import FastAPI
from pydantic import BaseModel, EmailStr

app = FastAPI(title="Production Service")

class UserRegisterDTO(BaseModel):
    email: EmailStr
    full_name: str
    age: int

@app.post("/api/v1/users", status_code=201)
async def register_user(dto: UserRegisterDTO):
    return {"status": "success", "user": dto.model_dump()}