Do You Want to Be an AI Engineer

Do You Want to Be an AI Engineer?

Can You Do This One Simple Thing?

Everybody wants to be an AI engineer right now.

The titles are flashy.

The job descriptions are ridiculous.

The salaries are intoxicating.

“Build RAG systems.”

“Fine-tune LLMs.”

“Own the end-to-end lifecycle.”

“Deploy scalable GenAI infrastructure.”

Sounds powerful.

But here’s the real question:

Can you do one simple thing?

🔥 The 90-Day Test

If you want to know whether you’re actually serious about becoming an AI engineer, don’t apply for the job.

Do this instead:

You take 90 days and:

• Build one clean RAG system

• Wrap it in FastAPI

• Put it behind API Gateway

• Add observability

• Document the architecture

That’s it.

No hype.

No LinkedIn titles.

No “AI visionary” bios.

Just build.

Step 1: Build One Clean RAG System

Not ten experiments.

Not a messy notebook.

One clean pipeline:

• Ingest real documents

• Chunk them correctly

• Generate embeddings

• Store them in a vector database

• Retrieve with relevance scoring

• Evaluate the responses

If you can’t explain:

• Why you chose that embedding model

• Why you chose that chunk size

• Why you chose that vector store

You’re not engineering yet.

You’re experimenting.

There’s nothing wrong with experimenting.

But don’t confuse it with architecture.

Step 2: Wrap It in FastAPI

Now expose it as a real service.

Not a notebook.

Not a CLI script.

An API.

Why?

Because AI in the enterprise is not a demo.

It’s a service boundary.

When you wrap it in FastAPI, you start thinking about:

• Request validation

• Error handling

• Latency

• Concurrency

• Versioning

Now you’re moving from “model tinkerer” to “systems engineer.”

Step 3: Put It Behind API Gateway

This is where most people stop.

They build something cool locally.

They never operationalize it.

Putting it behind API Gateway forces you to think about:

• Authentication

• Routing

• Throttling

• Deployment pipelines

• Environment promotion (Dev → QA → Prod)

AI is not magic.

It’s infrastructure.

Step 4: Add Observability

This is the part almost nobody talks about.

Can you answer:

• How long does retrieval take?

• How long does generation take?

• What is your failure rate?

• What does a bad answer look like?

• How do you detect hallucination patterns?

If you can’t measure it, you don’t control it.

If you don’t control it, you’re not engineering it.

Step 5: Document the Architecture

Write it down.

Explain:

• Why RAG instead of fine-tuning

• Why embeddings instead of keyword search

• Why this deployment model

• What the trade-offs are

If you can’t teach it, you don’t understand it.

Learn.

Teach.

Master.

Why This Is the Real Test

Anyone can:

• Prompt an LLM

• Install LangChain

• Spin up a vector database

Very few can:

• Turn that into a disciplined, production-ready system

And that’s the difference between:

“AI enthusiast”

and

AI engineer

The Hard Truth

Most job descriptions bundle:

• Model training

• RAG design

• MLOps

• Application engineering

• Platform architecture

Into one mythical human.

You don’t have to be mythical.

You just have to be systematic.

Build one real thing.

End to end.

Cleanly.

If you can do that in 90 days, you’re ahead of most of the noise.