AI Engineer vs ML Engineer

There is no universal boundary between AI engineer and machine-learning engineer. O*NET lists broad occupational families and alternate titles, while employers publish role descriptions that reflect their own products and teams. One current OpenAI Applied AI Engineer listing, for example, describes taking agent capabilities from demonstrations into dependable production tools; O*NET’s software-developer profile centers on designing, building and maintaining software. These are examples, not definitions for every organization. A useful working distinction is that some AI-engineering roles concentrate on applying existing models or APIs in user-facing systems: integration, prompt or workflow design, evaluation, data connections, reliability and product deployment. ML-engineering roles may concentrate more on training or adapting models, data and feature pipelines, model-serving infrastructure and monitoring. The tasks can overlap: an AI product may require model evaluation and software delivery, and a model engineer may build user-facing systems. Neither title guarantees that the person trains foundation models. To compare roles, mark the verbs in each posting: build, fine-tune, evaluate, integrate, deploy, scale or support. Ask what you will ship, how success is measured, who owns data and model quality, and how the work divides between research, infrastructure and product. Then identify gaps using evidence from the actual requirements. Treat title-based rules and universal skill lists with skepticism; the employer’s stated responsibilities and interview process are better evidence. Also check whether the role is expected to build prototypes, maintain production services or improve model quality; these goals can require different evidence even when titles match. Ask which users or teams receive the output, what failure looks like, and who can change the underlying model.