Production AI needs product engineers, not just model fluency

The distance between an AI demo and a reliable product feature is larger than most teams expect. The hard work lives in retrieval quality, evaluation, latency, cost controls, permissions, observability, and user experience.

That is why Andishi treats AI hiring as product engineering hiring. The best AI engineer for a startup can work across APIs, data, prompts, evaluation traces, background jobs, deployment, and interface states.

Model fluency matters, but it is not enough. Production teams need engineers who can explain tradeoffs, measure behavior, and keep a feature useful after the launch announcement has faded.