The future isn't about being an "AI Engineer" vs a "Fullstack Engineer."
It's about being an engineer who happens to use AI when it's the right tool.
Just like we use databases, queues, and caches.
It's just another tool in the box.
The best AI engineers aren't the ones adding GPT to everything.
They're the ones who know when NOT to use AI.
Here's what 2 years of shipping AI features taught me: 🧵
Build AI features like you build backend systems:
- Idempotent operations
- Graceful degradation
- Observable metrics
- Cost budgets (seriously, watch those API calls)
The fundamentals don't change because you added a neural network.
When Indians Ship, things are mostly:
- under-priced (suffering from outsourcing still)
- overbuilt (Not perfectionism, just chaos mistaken for features)
- a button away from prod, because permissions