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AI engineering has quickly become one of the most valuable skill sets in tech
The problem is that most beginners have no clear idea what they should actually study
Some start with machine learning theory
Some get stuck endlessly watching tutorials
Others jump straight into prompts and agents without understanding APIs, backend basics, or how real products are actually built
The result is usually the same: a lot of confusion and very little practical skill
If your goal is to become an AI engineer, you don’t need to master every field of artificial intelligence
You need to learn how to build useful AI systems in the real world
<b>That means learning how to:</b>
• build end-to-end applications with LLMs
• work with model APIs such as OpenAI and Anthropic
• properly design prompts and context
• use structured outputs and tool calling
• add retrieval when needed
• deploy projects so people can actually use them
This guide was created to give you a practical 6-month roadmap
<b>The article is 10,000+ WORDS</b>, so reading it may take a few hours or even longer
But its real value is that for every skill you need to learn, there are resources and clear explanations of what to do
That way, within six months you can reach the level of AI engineering, and start using it for yourself already within the first 1-2 months
<b>Writing this article took more than 40 HOURS</b>, and I worked on it together with my friend @andy_ai0