I will build a production fastapi backend for your ai saas application


About this gig
Building an AI product and need a backend that can handle more than a demo?
I will build a clean, production-ready Python/FastAPI backend for your AI SaaS, RAG application, agent workflow, web app or internal platform.
Your backend can include:
- FastAPI REST APIs
- PostgreSQL and SQLAlchemy
- Authentication and role-based access
- OpenAI, Claude or Gemini integrations
- Vector databases and RAG APIs
- File/document processing
- Background jobs and webhooks
- Third-party API integrations
- Docker deployment
- Validation, logging and error handling
- Tests and technical documentation
I focus on scalable architecture and maintainable code rather than quick demo-only endpoints.
Please message me before Standard or Premium orders with your architecture, required integrations and deployment target.
Get to know Muhammad Hamza
AI Engineer for RAG, Agents and Business Automation
- FromPakistan
- Member sinceJun 2023
- Avg. response time1 hour
- Last delivery1 year
Languages
English
My Portfolio
FAQ
Can you connect the FastAPI backend to my existing frontend?
Yes. I can build APIs for an existing Flutter, React, Next.js, mobile, web, or desktop application. Please share the required frontend workflow and API expectations before ordering.
Which databases do you support?
PostgreSQL is my preferred choice for production systems. Depending on the project, I can also work with SQLite, MySQL, MongoDB, Redis, or a vector database.
Can you add authentication and user roles?
Yes. Standard and Premium projects can include JWT authentication, OAuth where appropriate, role-based permissions, protected endpoints, and user-management workflows.
Can you integrate OpenAI, Claude, or Gemini?
Yes. I can integrate supported LLM APIs such as OpenAI, Anthropic Claude, and Google Gemini into the backend based on your application's requirements.
Can you build a RAG or vector-search backend?
Yes. I can implement document ingestion, embeddings, vector search, retrieval APIs, source-grounded responses, and related RAG backend components when included in the project scope.

