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How to become an AI Engineer

Prompt and context design, tools, retrieval, orchestration, evaluation, and structured output. Build reliable agent apps end to end.

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The AI Engineer roadmap

Each topic below carries a study note, real reading, and practice questions in the app. A topic unlocks when you pass the one before it, so the order is the path, so you are never guessing what to learn next.

Phase 1AI application engineering lLM foundations

How LLMs work

LLMs are next-token predictors that operate on tokens, not characters or words.

Prompting fundamentals

A prompt is the instruction and context you give the model.

Model APIs and tool use

Model APIs take messages and parameters and return generated text or tool-call requests.

Phase 2Building with LLMs

Structured output

Downstream code usually needs machine-parseable output.

Sampling and determinism

Sampling parameters like temperature and top-p control randomness.

Cost and latency

Different models trade capability against price and speed.

Phase 3Retrieval and knowledge

Embeddings and RAG basics

Embeddings map text to vectors so similar meaning sits close together.

Grounding and citations

Grounding means answering only from provided context and attributing each claim to its source.

Context management

The context window is a scarce, costly resource.

Phase 4Agents and tools

Tool use and function calling

Agents act through tools defined by a schema: a name, description, and typed parameters.

The agent loop

An agent runs a loop: reason, call a tool, observe the real result, then decide the next action.

Multi-step workflows

Real tasks span several dependent steps.

Phase 5AI application engineering in production

Evaluation

You cannot improve what you do not measure.

Safety and guardrails

Production agents need input/output guardrails.

Observability and deployment

Once live, you need to see what the system does.

Reading for this path

The primary sources behind the topics above, all free to read.

Coming from another job?

Most people on this path arrived from somewhere else: engineering, analysis, testing, product, support, compliance, design. Onboarding asks what you do today and builds a short starter run out of the gaps, so you begin from what you already know rather than from chapter one. See how it works.

AI Evals Engineer

Benchmark design, LLM-as-judge, regression suites, and quality metrics. Measure what models actually do before and after every change.

AI for Science Engineer

Modeling in biology, chemistry, physics, and materials, plus research tooling. Apply AI to scientific discovery.

AI Governance Analyst

Regulatory compliance, model cards, risk frameworks, and audit trails. Keep AI systems accountable and inside the rules.

AI Infrastructure Engineer

Data pipelines, infrastructure, deployment, observability, and cost control. Keep data, infra, and models reliable and cost-aware.

AI Integration Engineer

APIs, enterprise systems, legacy migration, and AI-feature rollout. Wire AI capabilities into the software businesses already run.

AI Platform Engineer

Internal SDKs, model gateways, routing, and guardrail infrastructure. Build the platform every team ships AI on.

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Start the AI Engineer path for free

The full study notes, the reading, and the practice questions behind every topic above are in the app. Answer one tonight and you have started.

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