Someone who has no idea what they're doing, like not even the slightest clue. They spend 90% of their time curating datasets, call every executable file a "notebook," require detailed instructions on how to submit a pull request, hard-code user specific variables, forms URLs with query strings using string concatenation, doesn't read manpages, and listens to terrible music
You don’t, stop adding the term “engineer” to any new trendy phrase, like “prompt engineer”! It lost its meaning and it doesn’t make you look professional either, it’s like calling yourself “phones doctor!”..
NFT bros had their pixel avatars, AI has Stable Diffusion generated avatars :D For some reason you can't use a real photo in either of these fields of work.
What really defines an AI engineer:
A) Already used their $5 on OpenAI API
B) Has llamacpp/ollama etc. in their CLI with a library of models
C) Has 64GB+ VRAM which makes computer extremely loud possibly gas-powered
AI engineer is more of a combination of data engineer and MLE. An AI engineer would help productionize an algorithm or AI model developed by the researchers which would include deploying at scale to compute, providing access to trained models, and ability to update and serve the models.
Basically researchers develops on his laptop, AI engineer helps deliver it to customer's laptop.
Here's a web app engineer perspective: delivery to the customer's laptop is likely over HTTP so the mechanism is taking customer text input, calling API's and delivering results.
In its simplest form (again from a web app engineer's viewpoint) LLMs ingest text, organize that data (a very complicated process I don't understand fully, nor need to), and provide API's to output text given some set of inputs so this resembles very closely working on Elastic or other search engine technology. A caveat is the API's being called likely maintain state in the sense of keeping track of inputs, context and outputs. I would classify someone that is working on this as more of an API/backend engineer. They need to understand the AI/LLM data model being used which is very specific and the use cases around it but they did not engineer the AI/LLM data model themselves, it was likely some other R&D engineers.
Edit to add: AI engineer to me is the R&D people I reference above - the ones building the data model that others use.
Titles are meaningless in general. Within a well organized company it should be defined with a job spec. A
job
ad should describe the
roll behind the title.
At my company they have ML engineer positions. However, I've found that most of them are just devs building APIs to interact with the model, or being a data jockey for the model. Basically, titles are almost meaningless.
In the USA, anyone can be an engineer. All they require is the will to call themselves an engineer. (Except for a few specific types). Eg, customer service engineers are now a thing
