Definition: AI engineering is the discipline of building production software around AI models, combining software engineering, data work and model integration so that AI features are reliable, measurable and maintainable.
Retrieval pipelines for RAG, AI agents with tool use, prompt and context management, evaluation suites, guardrails, gateways, and the APIs and user interfaces that put models in front of users.
ML engineers traditionally train and deploy models from data, with heavy statistics and feature work. AI engineers more often start from a pretrained foundation model and focus on integrating, adapting and evaluating it inside a product. The roles overlap, and strong teams mix both.
Solid backend engineering, usually in Python or TypeScript; working knowledge of model APIs and open-weight models; data handling; testing and evaluation habits; cloud deployment; and judgment about cost, latency and failure modes.
A demo can be built in a day. Production needs monitoring, versioned prompts, fallbacks, security review and a way to tell whether a change made the system better or worse.
Ryz AI engineers are senior and work US business hours. Compare AI engineer rates or scope an AI pod.
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