how do i become an ai software engineer?

the prerequisite nobody skips

Ordinary software engineering. Apis, databases, queues, deployment, testing, and reading someone else's code without flinching. Almost every effective ai engineer I have worked with came from backend or data engineering, not from a machine learning course, because the job is mostly systems work with two unusual constraints attached.

If that base is missing, spend six months there first. It is the fastest route, even though it does not feel like progress toward the goal.

the three projects that actually get you hired

One: retrieval over your own data. Take a corpus you know well, chunk it, embed it, index it, and answer questions against it. Then make it good, which is the whole exercise. Chunking strategy, reranking, and citation quality are where the learning is, and everybody's first version is worse than they expected.

Two: an evaluation harness. Twenty to fifty real inputs with known good outputs, an automated score, and a way to compare two versions. This is the skill teams complain most about missing, and it is the one that separates people who can improve a system from people who can only change it.

Three: an agent that completes a task with tools. Two or three tools, a step budget, structured output validation, full logging. Run it on twenty real tasks, count completions, and write down every failure with its cause.

Three finished projects with honest write-ups beat any certificate, because the write-up is the evidence you can measure.

what to actually learn, in order

1. Tokens, context, and cost. How pricing works and why context length is an architecture decision.
2. Embeddings and retrieval. Similarity search, chunking, reranking, and the reasons naive vector search disappoints.
3. Structured output. Schemas, validation, and what to do when the model returns something that does not parse.
4. Evaluation. Golden sets, scoring, regression tests, and the discipline of measuring before optimising.
5. Agent loops. Tool schemas, error recovery, and stop conditions.
6. Serving. Caching, batching, streaming, fallback between providers.

Deep learning theory is optional for the application tier and mandatory for the research tier. Be honest with yourself about which one you are aiming at, since the paths barely overlap. What does an ai software engineer do breaks the tiers down.

the realistic timeline

From a working engineering base, six to twelve months of consistent effort gets you employable at the application tier. From no engineering background, add eighteen months for the fundamentals and expect the first job to be ordinary backend work at a company that sells ai, which is a perfectly good entry point.

The research tier, and the $900,000 packages that get written about, is a different route entirely: a graduate degree, published work, and access to hardware most people never touch.