AI Platform Backend Development
Budget: ₹750 – ₹1,250 INR
I’m building an AI-powered platform whose core function is automated decision-making, and I want rock-solid Python backend support to move it from concept to production. The stack is already defined—FastAPI (or Flask where appropriate), SQLAlchemy with PostgreSQL, containerised through Docker—so you’ll be able to dive straight into clean, familiar code.
Here’s what I need from you:
• Design and implement RESTful (and, where useful, async) APIs that expose our decision engine to internal and external consumers.
• Wire those endpoints to existing and upcoming AI models so inference calls feel native in the service layer.
• Optimise response times and throughput; latency targets are sub-200 ms for the common paths.
• Deliver well-structured, documented code plus Docker files that let the team spin up identical environments in one command.
I’ll provide model specifications, existing database schemas, and access to a staging cluster. You’ll push to a Git-based workflow with CI tests; merge requests should include unit coverage for critical logic and simple Postman collections that prove the endpoints work as advertised.
If you’ve scaled FastAPI or Flask services before, understand async quirks, and enjoy translating ML outputs into reliable, deterministic APIs, let’s talk.
Here’s what I need from you:
• Design and implement RESTful (and, where useful, async) APIs that expose our decision engine to internal and external consumers.
• Wire those endpoints to existing and upcoming AI models so inference calls feel native in the service layer.
• Optimise response times and throughput; latency targets are sub-200 ms for the common paths.
• Deliver well-structured, documented code plus Docker files that let the team spin up identical environments in one command.
I’ll provide model specifications, existing database schemas, and access to a staging cluster. You’ll push to a Git-based workflow with CI tests; merge requests should include unit coverage for critical logic and simple Postman collections that prove the endpoints work as advertised.
If you’ve scaled FastAPI or Flask services before, understand async quirks, and enjoy translating ML outputs into reliable, deterministic APIs, let’s talk.