AI Solar Quote SaaS Platform

I’m launching an AI-driven, Australia-focused SaaS that instantly designs and prices residential solar systems from nothing more than a street address and the customer’s latest power bill. I need an experienced back-end developer comfortable in .NET 6+ or Python (FastAPI, Django, or similar) who can own the core service layer, then coordinate with a React (or Flutter Web) front-end to deliver a polished MVP.

Core scope
• Address-based solar design – pull roof geometry, sun exposure, and local tariffs to auto-size PV arrays.
• Power-bill analysis – read kWh, rate blocks, and feed-in tariffs to model payback.
• Customisable solar quote – dynamically assemble panel/inverter options, rebates, and pricing in AUD.
• Account system – social login (Google, Facebook, Apple), role-based authorisation, separate user & admin views, designed for eventual multi-tenancy.
• Integrations – REST hooks for leading CRM systems (e.g., HubSpot) and payment gateways (Stripe preferred) so sales teams can convert quotes instantly.

Tech expectations
• Clean, well-documented API layer, Swagger/OpenAPI specs, unit tests.
• GIS/mapping calls (Google Maps, Mapbox, or open alternative) for roof tracing.
• Simple rule-engine or ML model to size systems; I’m open to TensorFlow, sklearn, or your proposal.
• React (preferred) components for quote builder, dashboard, and admin panel; Tailwind or Material-UI welcome.
• CI/CD pipeline to Azure or AWS, containerised with Docker.

Delivery milestones
1. Architecture plan and data model.
2. Functional prototype that generates a system design and rough quote.
3. Front-end integration with secure auth and role separation.
4. CRM + payment hooks, final polish, and deployment script.

I’m available daily for stand-ups, and I’ll supply sample bills, postcode tariff data, and brand assets. Please share relevant SaaS or solar/energy projects, your chosen tech stack, and a rough timeline so we can kick off.
I am an engineer myself so you will work with me.