[csh0]

The “AI Software Engineers” at my company, whose applications and workflows I often support as an SRE, by and large build things with LLMs rather than train or create them.

What they build ranges from product features to internal tools. They make heavy use of LLM vendor inference APIs, vector databases, etc. They end up writing a lot of glue code and software to manage the context of the LLM, query for data, integrate with other systems and so on. They also develop front-end interfaces for their applications.

Only recently have they started to, lightly, explore the idea of training LLMs with managed services like AWS SageMaker.

All this is to say that, the “AI Engineer/SWE” title will probably represent vastly different things depending on the technical sophistication of the organization.

If someone told me they were an AI Engineer at OpenAI I’d be more inclined to expect their role to be more fundamental, elsewhere, not so much.