The Rise of the AI Engineer

Emergent capabilities are creating an emerging title: to wield them, we’ll have to go beyond the Prompt Engineer and write *software*.

We are observing a once in a generation “shift right” of applied AI, fueled by the emergent capabilities and open source/API availability of Foundation Models.

A wide range of AI tasks that used to take 5 years and a research team to accomplish in 2013, now just require API docs and a spare afternoon in 2023.

However, the devil is in the details – there are no end of challenges in successfully evaluating, applying and productizing AI:

Models: From evaluating the largest GPT-4 and Claude models, down to the smallest open source Huggingface, LLaMA, and other models

Tools: From the most popular chaining, retrieval and vector search tools like LangChain, LlamaIndex, and Pinecone to the emerging field of autonomous agents like Auto-GPT and BabyAGI (must-read recap from Lilian Weng here)

News: On top of this, the sheer volume of papers and models and techniques published each day is exponentially increasing with interest and funding, so much so that keeping on top of it all is almost a full time job.

I take this seriously and literally. I think it is a full time job. I think software engineering will spawn a new subdiscipline, specializing in applications of AI and wielding the emerging stack effectively, just as “site reliability engineer”, “devops engineer”, “data engineer” and “analytics engineer” emerged.

The emerging (and least cringe)1 version of this role seems to be: AI Engineer.

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