(Hat) AI Engineer

 AI Engineer is probably one of the most popular roles right now. Many people aspire to be AI Engineer without fully understanding on what the role is about. 

In this article, we will look at:

What is an AI Engineer? What an AI Engineer does? How is different from the other AI roles that people might get confused with?

AI Engineer:

- is responsible for designing and developing AI Models that can be used in various applications

- work across different stages of the AI Development life cycle, including data collection, pre-processing, model training, and deployment

- integrate the AI models into existing systems and ensuring they function correctly with a broader architecture

- also fine-tune models for efficiency and accuracy

What is the value that AI Engineers bring to an Organisation?

That question must be answered by what is the value that AI brings to an organisation. Ultimately, for any organisation where AI is not the product or service that they provide, it is about 2 things only cutting down cost or making more revenue. It might be saving money through faster decisions or making more money with more accurate decision but at the end of the day, it is about money.

An AI Engineer's value-addition would be understanding the domain of the customer and fine-tuning the AI models to better serve in their domain.

How is the AI Engineer role different from other roles in AI such as an ML Engineer?

From my research and understanding, I believe that the primary focus of AI engineering lies in software and data engineering. An AI engineer's role is to identify the appropriate AI model to execute specific tasks in suitable contexts. 

AI engineers have responsibilities in the application layer. They are more focus on the practical integration of their AI models with the a strong software development practice in mind. 

ML engineers concentrate on training and optimising models, they focus on model training and bias tuning. 

Conclusion:

IT is a demanding field. It is constantly changing and ever-evolving. Adapting the life-long learner approach and a curiosity for things is important. There needs to be a inner-thirst for knowledge. To be a techie. And guess what, it is doable. You do not have to be the smartest or the most hardworking. But, you must be consistent and have the desire. 

It is about being about to understand the nature of work and learn to appreciate it. Of course, it also comes from our innate desire to live up to our potential. 

I was not always a techie. There was a time in life that I did not believe that IT was for me. I only chose IT as a method of survival. But, my undergraduate study changed it. I am not apt in all fields of IT but I can be in certain parts. 

Then, the advent of  AI has made learning more easier and fun. True that I am not a great developer. But, with AI, I can do development with more ease. I can debug faster with more confidence. 

I have come to realise that no matter what field of IT, we might be interested in, DevOps or AI, Software development is the core. 

I have come to appreciate SDLC more as I learn about DevOps and AI. 

Becoming a good AI Engineer require being a good software engineer and data engineer or at least developing knowledge and confidence in those fields. 

Because AI is nothing with good, valuable data and integrated software.  Would you just buy a car engine without fuel or the other body parts.