How to become an AI engineer, step by step
Most developers have called an OpenAI API and built a demo. Very few can build a RAG pipeline that survives messy production data, design an agent that does not fall apart under load, or explain to their CTO why they chose one architecture over another.
I'm going to walk through exactly what separates the two, the same three steps I use to hire and train AI engineers. Then, if it makes sense, we get on a call.
No pitch. If it is not the right fit, I will tell you.
Most people learn the wrong things
A good engineer decides to get into AI, opens a browser, and within forty minutes is watching a video about backpropagation. Nobody is hiring you to train a model. They're hiring you to take models that already exist and make them useful inside a business with messy data, weird requirements, and a compliance team.
The rest stall out the same way. They build chatbots that only talk, when a thing that changes the state of a system is software. They learn a framework before the primitives, so when the framework changes, they own nothing. They never write an eval, so they have no idea if their thing actually works.
Companies are not short of applicants for these roles. They are short of engineers who have actually built these systems, which is why the listings sit open for months.
The good news: this list is short, learnable, and most of it is documented for free by companies that want you to use their products. Here's how, step by step.
Step 1: Get good at RAG
Retrieval augmented generation. Every tutorial stops at "chunk your docs, embed them, stick them in a vector database, profit." That version does not survive contact with a real corpus.
Chunking strategies
How you split a document determines everything downstream. Split on token count and you'll cut a table in half.
Metadata and filtering
The single highest-leverage thing on this list, and the thing most people skip. Filter first, then search.
Hybrid search
Dense vectors are great at meaning and terrible at exact matches. Sparse vectors handle part numbers and error codes. You run both and fuse the results.
Reranking
Retrieve 50, rerank down to 8, send those. A cross-encoder reranker fixes a shocking number of "the answer was in there somewhere" problems.
Step 2: Build production agents
Strip away the frameworks and the Twitter threads, and an agent is two things: context, and the ability to execute on tasks. Context is what the model knows when it makes a decision. Execution is the part almost every tutorial skips, and it's the part that gets you hired.
Your agent has to be able to change something, not just answer. Update a row in a database. Move a deal in a CRM. File a ticket. The moment an agent can write to an external system, permissions and failure modes get real, which is why human in the loop is the one pattern I'd learn cold: the agent reads freely, and before it does anything destructive, it stops and asks.
Step 3: Prove it works, then watch it
Agents are non-deterministic. You cannot unit test them, so you write evals instead. Start with retrieval quality: collect thirty or forty real questions, note which documents should come back, and now you can change a chunking strategy and know within a minute whether you made things better or worse.
Then there's observability. If your agent is running in production and you can't reconstruct what it did, you don't have a product, you have a rumor. Log the inputs, the retrieved chunks, the tool calls, the output, the latency, the cost. The tool matters far less than the habit.
I went from full stack engineer to AI engineer over about two years, by working at startups that needed someone to build this stuff and having nobody better available. Then I designed the AI engineering interview loop at the last startup I was at, which means I've been on the other side of the table watching people fail it.
These days I also help recruit for these roles, and I see the same skills come up over and over. It's almost boring how consistent it is.
Brian, founder of Parsity
What Developers Are Saying
"This course has taken me from wondering how working with RAG, agents, and LLMs work, to now building these things myself, with the understanding of exactly what's happening. It has empowered me to learn by building."
"Brian's course cut through so much of the AI hype fog for me. He provided super practical pathways into implementing AI within very real world circumstances."
"I now have the confidence and skills to go out into the world and build awesome agentic RAG apps."
The title on the job posting will keep changing. What they're testing for won't.
Skip the math. Learn RAG properly. Build agents that can change things, not just talk. Write evals. Log everything.
If you want to talk through where you are and whether this is the right next step, grab 15 minutes.
No pitch. If it is not the right fit, I will tell you. Questions? assistant@parsity.io
