The 10 biggest career mistakes I see AI engineers make.

Avoiding even half of these puts you ahead of most.

1. Chasing frameworks instead of fundamentals. LangChain changes. Linear algebra doesn't.

2. Collecting certificates instead of building projects. Your 15th certificate has zero marginal value.

3. Staying too long in a role with no AI deployment. If you're "building AI" but nothing reaches production after 18 months, leave.

4. Ignoring soft skills. The best model in the world doesn't matter if you can't explain it to stakeholders.

5. Not negotiating compensation. The market favors you. Use that leverage.

6. Over-specializing too early. Build breadth first (years 1-3), then specialize (years 3-5).

7. Working in stealth. Build in public. Share what you learn. Your visibility creates opportunities.

8. Ignoring MLOps and deployment. The gap between "model works" and "model is in production" is where careers are made.

9. Comparing yourself to curated online personas. Nobody posts about their failed experiments. Everyone struggles.

10. Waiting until you're "ready" to start. You'll never feel ready. Start anyway. Build. Ship. Learn. Repeat.

Every one of these mistakes cost me months or years of progress. I'm sharing them so you can skip the pain and accelerate the growth.

The AI career race isn't won by the smartest. It's won by the most strategic. Be strategic.