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AI Engineer Career Path

A practical path from the fundamentals to job-ready ai engineer work — with the skills, projects and interview prep for each stage.

6-9 monthsEstimated duration
Intermediate to AdvancedDifficulty
5Core skills
3Projects with rubrics

Last updated 27 August 2026

Step 1 of 4

Python and APIs

Stay on this stage until you can use it without a tutorial open. Finish it with something you can show — a repo, a write-up or a shipped change.

Skills this stage feeds

Python OpenAI APIs RAG Vector databases Evaluation

Mini project

Document Q&A — brief and rubric

Resume tip Write the bullet for this stage the day you finish it — what you built, with what, and what changed because of it.
Interview question to expect How do embeddings work?

Skills to prove

Python OpenAI APIs RAG Vector databases Evaluation

Naming a skill is not enough — each one needs a project or a work example behind it.

Interview questions to expect

  • How do embeddings work?
  • How do you evaluate an LLM feature?
  • When would you use RAG?
Practice a question today
QUESTIONS? WE'VE GOT ANSWERS

Roadmap questions

Before you start the AI Engineer path

How should I pick a roadmap?
Choose the roadmap that matches the job descriptions you want to apply for, then adjust it for your current strengths and timeline.
Do I need every certification listed?
No. Certifications are optional signals. Projects and demonstrable skills usually matter more than collecting credentials.
How do I know I am ready to apply?
When you can explain your projects, pass basic interview questions, and show a resume that matches the role's core requirements, start applying while you keep improving.
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