📌 Key Insights:

– AI Engineer Defined: A generalist well-versed in LLM frameworks, proficient at integrating it with private, multimodal data using the leanest infrastructure (like Vector DBs such as Deep Lake and tools like LangChain or LlamaIndex). All of this at optimal costs.

– Soft Skills on the Rise: Today’s AI engineer needs UX aptitude, empathy, and a keen sense of domain knowledge.

– Infrastructure Know-How: Especially with the current GPU shortage, understanding when and how to optimize resources (like using CPUs for fine-tuning models) is crucial.

– Academia’s Pace: While AI engineering is surging, academic institutions are lagging, boosting the demand for specialized training.

(from a LinkedIn post by Mikayel H. on 9/9/2023)

# Feedback:

The post provides key insights about the AI engineering field, emphasizing the importance of being a generalist and well-versed in LLM frameworks. It also highlights the rise of soft skills, the need for infrastructure know-how, and the lagging pace of academic institutions in meeting the demand for specialized training.

# Actions to improve the content:

1. Provide examples or anecdotes to support the key insights mentioned. This will help readers better understand and relate to the information.

2. Add a brief explanation of what LLM frameworks are and why they are important in AI engineering.

3. Expand on the significance of soft skills in AI engineering, explaining how they contribute to successful projects and collaborations.

4. Include specific strategies or techniques for optimizing resources in the current GPU shortage situation, such as using alternative hardware or parallel processing.

5. Offer suggestions or resources for individuals seeking specialized training in AI engineering, considering the gap between industry demands and academic offerings.

Overall, these improvements will enhance the clarity and depth of the content, providing a more comprehensive understanding of the AI engineering field.