AI-Ready Full-Stack Web Development
Budget: ₹100 – ₹400 INR
I’m looking for a full-stack partner who can move fast, write clean code, and—crucially—add the right touch of AI where it actually makes sense. You’ll be working directly with me (I code too), so expect straight technical conversations and quick feedback loops rather than formal hand-offs or endless meetings.
Scope
• Platform: Web applications only.
• Front end: Next.js is my default choice; if you need the underlying React layer for something specific, just say so.
• Back end: Typical Node.js/TypeScript stack is in place, but I’m flexible as long as the API layer stays lean and well-documented.
• AI layer: You should already be comfortable deciding when to call an OpenAI endpoint, spin up a lightweight TensorFlow model, or rely on a Scikit-learn pipeline—whatever gets the job done accurately and efficiently. The end goal is production-ready features, not just proof-of-concept demos.
What will keep us moving
– You push to Git (or a comparable repo) daily.
– PRs come with a brief explanation of the AI technique used and how it slots into the existing architecture.
– Tests are part of the delivery, even if they’re stubs for now; we’ll expand coverage together.
– Clear communication: if something is off-scope, tell me immediately rather than silently reinventing.
Acceptance
A task is complete when it:
1. Runs locally and in our staging environment without manual fixes.
2. Meets the performance target we set at the start of the task.
3. Includes concise documentation for any AI component (model choice, API parameters, fallbacks).
If you thrive in a quick, no-nonsense workflow and know how to weave AI into real-world web products, let’s ship something great together.
Scope
• Platform: Web applications only.
• Front end: Next.js is my default choice; if you need the underlying React layer for something specific, just say so.
• Back end: Typical Node.js/TypeScript stack is in place, but I’m flexible as long as the API layer stays lean and well-documented.
• AI layer: You should already be comfortable deciding when to call an OpenAI endpoint, spin up a lightweight TensorFlow model, or rely on a Scikit-learn pipeline—whatever gets the job done accurately and efficiently. The end goal is production-ready features, not just proof-of-concept demos.
What will keep us moving
– You push to Git (or a comparable repo) daily.
– PRs come with a brief explanation of the AI technique used and how it slots into the existing architecture.
– Tests are part of the delivery, even if they’re stubs for now; we’ll expand coverage together.
– Clear communication: if something is off-scope, tell me immediately rather than silently reinventing.
Acceptance
A task is complete when it:
1. Runs locally and in our staging environment without manual fixes.
2. Meets the performance target we set at the start of the task.
3. Includes concise documentation for any AI component (model choice, API parameters, fallbacks).
If you thrive in a quick, no-nonsense workflow and know how to weave AI into real-world web products, let’s ship something great together.