15

stage · curriculum

Career

Less technical, more strategic. Where AI engineering is going, what to build to get hired, how to stay current. People who build, write, and ship in public have radically different trajectories than those who only read.

3 articles
15 min to read
6 books
if you only do one thing

Ship something with your name on it. The technical content from Stages 1–14 is necessary but not sufficient; public output is the multiplier.

Articles in this stage

  1. 01 AI Engineer Roles
  2. 02 Learning Roadmap
  3. 03 Staying Current

Stage 15 — Engineering & Career

Less technical, more strategic. Where AI engineering as a profession is going, what to build to get hired, and how to stay current in a field that moves every week.

Articles

  1. AI engineer roles — applied, research, infra, product
  2. Learning roadmap — what to build to get hired
  3. Staying current — feeds, papers, communities

MVU

You can:

  • Articulate what kind of AI work you want to do
  • Identify a 90-day project that demonstrates that role
  • Stay updated on the field without drowning in noise

Exercise

Ship something — a side project, a blog post, a fine-tuned model on HuggingFace, a contribution to an open-source agent. Anything public, with your name on it.

Why this stage matters

The technical content of Stages 1–14 is necessary but not sufficient. People who build, write, and ship in public have radically different career trajectories than those who only read.

See also

  • All previous stages.

Further reading

Books move slower than papers in this field — treat these as foundations, not replacements for the latest research. Real authors, real publishers, real editions. Free badges mark books with author-authorized full text online.

  1. The Manager's Path cover

    The Manager's Path

    Camille Fournier

    O'Reilly, 2017

    The canonical book on going from senior IC to lead to manager to director.

  2. The Pragmatic Programmer cover

    The Pragmatic Programmer

    Andrew Hunt, David Thomas

    Addison-Wesley, 20th anniv. ed., 2019

    The timeless career-spanning fundamentals.

  3. Designing Data-Intensive Applications cover

    Designing Data-Intensive Applications

    Martin Kleppmann

    O'Reilly, 2017

    The systems-engineering book the AI-eng stack assumes you've read.

  4. Designing Machine Learning Systems cover

    Designing Machine Learning Systems

    Chip Huyen

    O'Reilly, 2022

    The canonical pre-LLM ML systems book. Chapters 3, 4, 5, 8, 10 are still core.

  5. Building Machine Learning Powered Applications cover

    Building Machine Learning Powered Applications

    Emmanuel Ameisen

    O'Reilly, 2020

    Idea-to-product walkthroughs that complement the case-studies arc.