Who is an AI engineer?

AI Engineering: The Emergence of a New “On-Demand” Job Role

Richard Warepam
Dare To Be Better


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Are AI engineers and data scientists the same?

Are AI engineers and ML engineers the same?

What do you think? – Most people might say that ML engineers and AI engineers are the same. But it’s not totally true.

Well, it’s still the early days for such a job role. So, no wonder there is so much confusion.

Let me try to paint a clear picture. See if you get it, or maybe you just don’t agree with what I think it is.

AI Engineer?

Pre-Foundational Model Phase

Before the advent of foundational models like GPT, Claude, and others, the tech industry had a distinct division of roles:

  • Data Scientist: Their primary role was to gather and prepare data and construct models that could be integrated into the company’s software. For instance, let’s consider the creation of a chatbot.
  • Software Engineer: They were responsible for building and maintaining the software. They would take the models developed by the data scientists and incorporate them into the software.

However, there was a gap. Data scientists typically lacked extensive knowledge about software development, and software engineers were often not well-versed in data science.

This led to communication issues whenever a problem arose.

  • That’s when the role of the ML engineer emerged. ML engineers are proficient in both data science and software development, acting as a crucial intermediary in the process.

They bridged the gap, enhancing the efficiency and effectiveness of the entire operation.

Post-foundational Model Phase

In the current era, most general tasks, such as those related to chatbots or other NLP-related tasks, can be accomplished using large language models (LLMs).

These models are not only more accurate but also offer greater flexibility compared to custom models built by individual companies.

This shift has led to a significant change in roles within the tech industry.

The tasks that were once handled by data scientists and ML engineers for these general tasks are now being taken over by these foundational models developed by big tech companies.

Software engineers can integrate these models into their software using APIs.

However, the challenge lies in the fact that these engineers often lack the necessary training and knowledge about these models.

They may not be well-versed in areas like deep learning, prompting, fine-tuning, etc., which are crucial for maximizing the utility of these models and addressing any issues that may arise post-deployment.

This gap has led to the emergence of a new role—the AI engineer. This role serves as an intermediary between the software engineers and the foundational models.

So, what does an AI engineer do? Here’s a brief overview:

  1. They should have a basic understanding of LLMs and foundational models.
  2. They should be proficient in fine-tuning these models.
  3. They should be skilled at prompting these models for optimal results.
  4. They should possess skills like tooling and LLMops.

This is a basic snapshot of the role.

If you’re interested in a comprehensive roadmap for this emerging and high-demand role, let me know in the comments. I’ll write an article based on the response.

I hope this gives you a clearer picture of this new role. But I’m sure you still have questions, right?

Then, are data scientists and machine learning engineers becoming obsolete?

The answer is a definitive no. You might be curious as to why.

Here’s the reason: foundational models, while capable of addressing most of the general use cases that every company requires, fall short when it comes to the unique, intricate cases that each company handles in its own way.

Take Netflix, for example.

Netflix’s recommendation models and other specific models unique to its platform cannot be created using foundational models.

This is where the expertise of data scientists and machine learning engineers becomes crucial.

They work on the core use cases for the company, while AI engineers handle the general use cases.

So, the need for these professionals is not going anywhere anytime soon. They remain an integral part of any tech-driven company’s team.

Wrapping Up:

Based on my research and understanding, I firmly believe that “AI engineer” will be a highly demanded job in the coming years.

Image by Author

The advent of foundational models has significantly changed the landscape of the tech industry, creating a new role—the AI engineer. These professionals serve as a crucial link between software engineers and foundational models, possessing skills in areas like deep learning, prompting, and fine-tuning.

While data scientists and machine learning engineers continue to play an integral role in handling unique, intricate cases specific to each company, AI engineers are becoming increasingly important for handling general use cases.

This shift indicates a growing demand for AI engineers, making it a promising career path for those interested in the intersection of data science and software development.

As the tech industry continues to evolve, the role of the AI engineer will undoubtedly become more defined and indispensable.



Richard Warepam
Dare To Be Better

Data Scientist & Writer | Google Certified Data Analyst | As a Mentor - Writes on Data Science and AI | My eBooks: https://codewarepam.gumroad.com/