My product’s UI is complete, the concept is validated, and I’m ready to move into testing—what’s missing is a robust Python backend plus a machine-learning layer that performs “best case scenario” matching for my users. I’d like this built with either Python or Go (whichever you feel is the better fit once we dig into the requirements). Core objectives • Develop an API-driven backend that connects cleanly to the existing frontend. • Implement the matching algorithm I have sketched out, transforming it into a production-ready ML model. • Set up data persistence; I’m open to SQL, NoSQL, or a hybrid approach and will lean on your recommendation for performance and scalability. • Provide authentication, basic admin tooling, and clear endpoints so we can push the product into a staging environment for user testing. What I’ll supply • Figma screens and the current React codebase for reference. • A written outline of the mathematical logic behind the matching system, along with edge-case notes and sample datasets. Deliverables 1. Well-structured Django/Flask project with REST (or GraphQL if justified) endpoints. 2. Machine-learning module encapsulated in its own service or reusable app, fully documented. 3. Database schema or migrations plus seed data. 4. Dockerfile or equivalent setup scripts so I can spin up the stack locally and on a staging server. 5. Brief hand-over session or video walkthrough. If you’re comfortable owning both the backend architecture and the ML implementation, let’s talk timelines and get this into testers’ hands.