AI engineer roadmaps

The path

1. 1What AI coding tools are good and bad at
2. 2Setup and project context
3. 3Prompting for code: small tasks, clear specs

+ 7 more

The path

1. 1Start from the task, not the model
2. 2Reasoning quality: when a bigger model pays off
3. Speed and cost per task

+ 6 more

The path

1. 1How LLMs work: tokens and context
2. 2Prompting and model APIs
3. RAG: embeddings, chunking and retrieval

+ 5 more

The path

1. 1Python for data
2. 2The maths you need
3. Data cleaning and feature engineering

+ 4 more

The path

1. 1How agents work
2. 2Tool calling from scratch
3. Structured output and validation

+ 7 more

The path

1. 1From notebook to package
2. 2Data and model versioning
3. Experiment tracking and the model registry

+ 7 more

The path

1. 1Security foundations for AI builders
2. 2How LLM apps and agents work, and where they break
3. Risk maps: the OWASP lists and MITRE ATLAS

+ 6 more

The path

1. 1Why AI governance, and the rules
2. 2AI inventory: know what you run
3. Data foundation

+ 5 more

The path

1. 1What AI is really doing to tech jobs
2. 2Read the signals and your own exposure
3. 3Be hard to cut at work

+ 4 more

The path

1. 1The roles compared
2. 2Core engineering you still need
3. Discovery and the customer's problem

+ 7 more

The path

1. 1What AI data work is and why it exists
2. 2The honest picture
3. Core annotation skills

+ 3 more

The path

1. 1What clients actually buy
2. 2Video fundamentals AI does not replace
3. The AI video tool stack

+ 5 more