AI Engineer Role

The AI Engineer (per Latent Space’s “The Rise of the AI Engineer,” relayed by Matt Pocock) is a software developer who builds applications powered by AI — and the role is defined negatively as much as positively:^[raw/aihero/what-is-an-ai-engineer.md]

You don’t need: linear algebra, building foundation models from scratch, or having read “Attention Is All You Need.” You do need: strong software engineering fundamentals, reliable/scalable application skills, modern AI tooling knowledge, and a user-experience focus.

The API Boundary vs ML Engineering

• AI Engineers build applications that use AI — orchestrating APIs, implementing RAG, building evaluation systems, and “writing lots of code in the hottest new programming language: English” (Karpathy).
• ML Engineers work below the API boundary: training, fine-tuning, model internals.

The Mindset Shift

Building with LLMs means trading deterministic input->output systems for probabilistic ones: define success criteria early, and build a culture of continuous improvement on real user data (see eval taxonomy, the Vibes-Only Trough to Data-Driven Slope journey). The role’s daily work spans llm message protocol mechanics, agents vs workflows architecture choices, and context budget audit cost discipline.

Related

eval taxonomy, agents vs workflows, llm message protocol, context budget audit, software engineering fundamentals for agents.