Full-Stack Mobile App Build
Budget: ₹75,000 – ₹150,000 INR
I’m building a web application that uses AI-based image generation as part of a creative workflow.
The app is currently fully functional as a prototype, built using Emergent but now needs to be made production-ready with a secure backend and scalable infrastructure.
You’ll work on setting up the technical foundation that allows the app to support 1,000+ users and hundreds of AI requests per minute, with a strong focus on speed, reliability, and data protection.
⸻
Scope of Work
• Build a secure backend (Node.js / Python) with user authentication (Supabase Auth or Firebase Auth).
• Set up a database (Supabase / PostgreSQL) for user data and job tracking with Row Level Security (RLS).
• Implement a background job queue (Redis + BullMQ / Cloud Tasks) to handle multiple concurrent AI generation requests efficiently.
• Connect to existing AI APIs through the backend with proper rate limiting, retries, and error handling.
• Deploy the backend on Fly.io / Render / Cloud Run with autoscaling to handle load spikes.
• Add logging, monitoring, and daily backups.
• Protect sensitive logic and data (handled server-side only, never exposed to users).
⸻
Preferred Tech Stack
• Backend: Node.js (Express/NestJS) or Python (FastAPI)
• Database: Supabase (PostgreSQL)
• Auth: Supabase Auth / Firebase Auth
• Queue: Redis (BullMQ or similar)
• Hosting: Fly.io / Render / Cloud Run
• AI Integration: Any major AI API
• Monitoring: Sentry / Logtail / Uptime Robot
⸻
You Should Have Experience In:
Designing scalable APIs that process AI workloads
-Managing queues and background jobs (Redis / BullMQ / Celery)
-Implementing secure authentication and data isolation (RLS, JWT)
-Working with AI APIs and asynchronous processing
- Deploying autoscaled apps on cloud platforms
-Writing clean, well-documented code with good error handling
⸻
Timeline
• Phase 1 (2–3 weeks): Backend setup, job queue, API integration, and deployment
• Phase 2 (1–2 weeks): Security layer (encryption, rate limits, backups) and load testing
The app is currently fully functional as a prototype, built using Emergent but now needs to be made production-ready with a secure backend and scalable infrastructure.
You’ll work on setting up the technical foundation that allows the app to support 1,000+ users and hundreds of AI requests per minute, with a strong focus on speed, reliability, and data protection.
⸻
Scope of Work
• Build a secure backend (Node.js / Python) with user authentication (Supabase Auth or Firebase Auth).
• Set up a database (Supabase / PostgreSQL) for user data and job tracking with Row Level Security (RLS).
• Implement a background job queue (Redis + BullMQ / Cloud Tasks) to handle multiple concurrent AI generation requests efficiently.
• Connect to existing AI APIs through the backend with proper rate limiting, retries, and error handling.
• Deploy the backend on Fly.io / Render / Cloud Run with autoscaling to handle load spikes.
• Add logging, monitoring, and daily backups.
• Protect sensitive logic and data (handled server-side only, never exposed to users).
⸻
Preferred Tech Stack
• Backend: Node.js (Express/NestJS) or Python (FastAPI)
• Database: Supabase (PostgreSQL)
• Auth: Supabase Auth / Firebase Auth
• Queue: Redis (BullMQ or similar)
• Hosting: Fly.io / Render / Cloud Run
• AI Integration: Any major AI API
• Monitoring: Sentry / Logtail / Uptime Robot
⸻
You Should Have Experience In:
Designing scalable APIs that process AI workloads
-Managing queues and background jobs (Redis / BullMQ / Celery)
-Implementing secure authentication and data isolation (RLS, JWT)
-Working with AI APIs and asynchronous processing
- Deploying autoscaled apps on cloud platforms
-Writing clean, well-documented code with good error handling
⸻
Timeline
• Phase 1 (2–3 weeks): Backend setup, job queue, API integration, and deployment
• Phase 2 (1–2 weeks): Security layer (encryption, rate limits, backups) and load testing