In this article I will dive into the role of the AI Engineer and the two emerging professions.
Why AI engineering now?
Artificial intelligence has been around for the past 70+ years and has generated significant revenue for businesses who are able to deploy it effectively. Most of the gains have gone to larger organizations like Google or Meta, rather than startups. Typically AI and machine learning were geared towards productivity and efficiency, or simply doing things smarter than before.
With the explosion of generative AI and powerful open source platforms available to anyone, the landscape has significantly changed. The technology has opened up new avenues, allowing people to fundamentally think differently about how business works. We are now seeing AI-first companies threaten the profits of their larger peers in direct competition with entirely new innovations making old ones obsolete.
When ChatGPT exploded in the AI world, Google issued a code red’ for their core offering- search, and the tech world took notice. Suddenly, instead of ten search results, billions of data points were available at your fingertips for whatever you were interested in. This flood of data, capital and innovation suggest that we could be in the early stages of a cycle that will transform our lives and the economy at levels we have not seen since the invention of the microchip and the internet.
What is an AI Engineer?
There are two broad categories emerging of the AI engineer: The Enterprise AI Engineer and the Startup AI Engineer.
The Enterprise AI Engineer
An Enterprise AI Engineer plays an integral role in designing, developing, and implementing artificial intelligence (AI) solutions tailored to meet the unique needs of businesses. These professionals combine their expertise in AI technologies with a deep understanding of industry-specific challenges to create intelligent systems that enhance operations, drive efficiency, and provide valuable insights.
Enterprise AI Engineers work closely with cross-functional teams, including data scientists, software developers, and domain experts. They analyze business requirements, identify opportunities for AI integration, and formulate comprehensive AI strategies that align with organizational goals. These engineers often serve as the glue for integrating AI in an organization and do not necessarily need to have a coding or data science background, but do need to be capable of productizing AI.
The Startup AI Engineer
A Startup AI Engineer is a position at the forefront of applying artificial intelligence (AI) technologies to shape the trajectory of emerging businesses. These engineers blend their technical expertise with entrepreneurial spirit to develop AI-driven solutions that propel startups forward, addressing challenges and seizing opportunities with new innovation and agility.
In a fast-paced startup environment, experimentation is key. These engineers rapidly create prototypes of AI concepts to test feasibility and potential outcomes. They design algorithms, models, and systems that can tackle specific problems, such as optimizing processes, enhancing user experiences, or automating tasks.
Proficiency in programming languages like Python, hands-on experience with AI frameworks (e.g., TensorFlow, PyTorch), and expertise in data manipulation and analysis tools. Startup AI Engineers should be dynamic, pivot when necessary, and have a willingness to quickly acquire new skills and knowledge.
What type of AI engineering role should I pursue?
It really depends on your background and aspirations. For an Enterprise AI engineering role in a larger organization, you will more likely need to have a degree in computer science and companies will want to see that you are adept at some combination of technologies including machine learning, data modeling, statistics, programming and databases. Suggested career paths include data scientist, data analyst and data engineer.
This is a blue ocean of opportunity, in Google’s recent Cloud Next event they mentioned: ‘Among organizations considering or using AI, 82% believe it will either significantly change or transform their industry’.
On the other hand if you are building your own company, or want to join a team as a startup AI engineer, you will need to have a much different mindset. Flexibility and the ability to learn anything quickly are core aspects of the job. If you have been wondering whether or not you should learn programming and specifically Python, coding could get you more quickly in the door at a startup.
But it could be argued at this point that machine learning and data science are better foundational pathways to being a startup AI engineer than being a programmer. Much of the boilerplate coding new programmers develop experience with is being replaced by no code systems at larger organizations so that anyone can build their own application now.
As a startup engineer you will likely be building something completely new and will not have the type of resource pool of a larger organization or the limitations working within existing systems. So the key distinction is: are you building a platform or app, or are you creating a system to run a company with AI?
If you are building a platform or app then yes, learn to code. If you want to be an AI engineer running a company, learn about all the no code systems, APIs and databases and how you can effectively create a complete system for companies. It is a fast moving and exciting space with limitless potential.