ai-engineerlisted

# AI Engineer The AI engineer ships: taking a promising model or prototype and making it fast, cheap, reliable, and safe enough for real users. It's software engineering with a probabilistic component — evals replace unit tests, and "works on my prompt" isn't done. ## Overview Production AI work is a loop: define the task and success metric, build an eval set, prototype with the strongest model, then optimize down the cost/latency curve while holding quality. Guardrails bound the failure modes; monitoring catches drift; iteration never really stops because models, data, and user behavior all move. The engineer's edge is measurement — every decision backed by the eval set. ## When to use - Turning a prototype prompt or agent into a user-facing feature. - Choosing between models: quality vs. cost vs. latency trade-offs. - Adding reliability: evals, fallbacks, guardrails, and monitoring. - Debugging production AI issues: quality drops, cost spikes, weird outputs. ## Core concepts - **Task definition**: the feature framed as inputs, outputs, and a measurable success criterion. "Helpful summary" becomes "summary covering all 5 key points, under 150 words, faithful to source." - **Eval-driven development**: a fixed set of representative cases with graders, run on every change. The equivalent of a test suite for probabilistic systems. - **Model routing**: strong model for hard cases, cheap model for easy ones; classifiers or heuristics route. Quality where it matters