Data and AI track
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.
Last updated 27 August 2026
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
Mini project
Document Q&A — brief and rubric
LLM fundamentals
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
Mini project
Resume feedback assistant — brief and rubric
Embeddings and retrieval
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
Mini project
Prompt evaluation harness — brief and rubric
Evaluation and safety
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
Mini project
Document Q&A — brief and rubric
Certifications worth the time
- DeepLearning.AI short courses
- Cloud ML fundamentals
Certificates open a conversation; they do not close one. Treat them as a reason to build something.
Portfolio projects
Skills to prove
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?
Roadmap questions
Before you start the AI Engineer path
How should I pick a roadmap?
Do I need every certification listed?
How do I know I am ready to apply?
Compare AI Engineer with a nearby role
Job titles overlap more than they look. Reading two adjacent roles shows which one your experience actually fits.
Check your own AI Engineer resume.
Upload it, paste a job description, and see the ATS score, missing keywords and weak bullets before you apply.