High-Performance Python FastAPI APIs
Building asynchronous REST endpoints, microservices, and AI/ML model deployment pipelines.
Services Offered
Asynchronous API Design & Microservices
Architecting high-concurrency APIs utilizing Python's `asyncio` loop. I design clean microservices with structural routing, sub-applications, middleware configuration, CORS security, custom dependency injection, and complete OpenAPI/Swagger automatic documentation.
AI/ML Model & LLM Pipeline Integration
Deploying machine learning models, LangChain workflows, vector similarity search systems, and LLM text generation/parsing engines. I design asynchronous processing wrappers to execute heavy neural networks, compute-intensive processes, or external API tasks without choking HTTP routing loops.
Pydantic Data Validation & Serialization
Defining robust data schemas with Pydantic V2. I enforce strict input validation, automatic error responses, type coercion, and high-speed JSON serialization. This guarantees type safety, clean schemas, and predictable application inputs/outputs.
Distributed Task Processing with Celery
Orchestrating distributed worker networks with Celery, Redis, and RabbitMQ. I handle long-running operations like PDF parsing, transcription, scraping, vector calculations, and image generation, decoupling them entirely from user-facing request loops.
WebSocket & Real-time Stream Gateways
Building bi-directional communication channels and Server-Sent Events (SSE) streaming connections. I specialize in streaming real-time AI generation results, candidate activity monitoring, push notifications, and live status updates directly to clients.
Architecture & Engineering Design
My FastAPI application design is built to leverage python's concurrent features while maintaining a highly structured, scalable codebase. I follow a domain-driven structure: 1. **Routers Layer**: Separated by feature domain (e.g. '/users', '/assessments', '/billing'). 2. **Services Layer**: Pure Python business logic functions completely isolated from HTTP requests. 3. **Database Repositories**: Data access layer managing database operations, keeping query logic clean and separated from business flow. 4. **Pydantic Schemas**: DTO (Data Transfer Object) models for request validation and response filtering.
I deploy these applications inside lightweight Docker containers managed by Gunicorn using Uvicorn worker classes ('uvicorn.workers.UvicornWorker'). To handle heavy resource-intensive tasks, I scale out independent stateless Celery worker nodes, ensuring the main API remains fast, responsive, and resilient to sudden spikes in backend load.
Proven Track Record
I have over five years of professional experience developing backend systems with Python, specializing heavily in the FastAPI ecosystem. During this time, I have designed and deployed APIs that serve as the backbone for AI-driven platforms, RAG real-estate tools, and background analytics dashboards.
A notable success was architecting the backend for RecruitEase Pro, a large AI recruitment platform. I utilized FastAPI's asynchronous handlers, integrated Celery workers for heavy resume processing, and used WebSockets for candidate monitoring. The result was a system capable of managing high-concurrency recruiter operations while keeping response latencies under 50ms. I focus on optimizing SQLAlchemy performance, configuring async connection pools (using 'asyncpg' or 'aiomysql'), and writing robust unit tests using pytest.
Tooling & Ecosystem
Python Stack
AI & Vector
Container & DevOps
Related Portfolio Projects
Need a High-Performance Python Backend?
Let's build a type-safe, asynchronous FastAPI backend tailored for high traffic, complex AI workflows, or lightweight microservices. Contact me to map out your high-performance Python system.
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