Top 6 Most Demanding AI Jobs In 2023!

Most likely, you've heard about the AI revolution taking place -- both the good and the bad. If you believe the AI hype machine, AI will have an impact on every aspect of our lives, making jobs easier and more efficient. If you believe some alarmists, it will take almost everyone's jobs.

While AI may cause some job loss, there will also be many AI opportunities and benefits for businesses. Many of today's job skills can also be applied to AI development.

However, the demands for skills and training can be rather high. AI is not trivial programming. People need to be well trained and well educated in other areas, in addition to computer science and programming.

10 top AI jobs

AI jobs are changing at a fast pace, just like technology. Here are a few of the top AI jobs to check out.

1.AI product manager

An AI product manager is similar to other program managers. Both jobs require a team leader to develop and launch a product. In this case, it is an AI product, but it's not much different from any other product in terms of leading teams, scheduling and meeting milestones. AI product managers need to know what goes into making an AI application, including the hardware, programming languages, data sets and algorithms, so that they can make it available to their team. Creating an AI app is not the same as creating a web app. There are differences in the structure of the app and the development process.

2. AI research scientist

An AI research scientist is a computer scientist who studies and develops new AI algorithms and techniques. They develop and test new AI models, collaborate with other researchers, publish research papers and speak at conferences. So, programming is only a small portion of what a research scientist does. The tech industry is extremely open to self-taught and non-formally trained programmers, but it makes an exception for AI research scientists. They need to have a strong understanding of computer science, mathematics and statistics. Typically, they need graduate degrees. 

3. Big data engineer

 AI works with large data sets and so does its precursor, big data. A big data engineer is similar to an AI engineer because they are responsible for designing, building, testing and maintaining complex data processing systems that work with large data sets. But, instead of working with GPT or LaMDA, they work with big data tools, like Hadoop, Hive, Spark and Kafka. Like AI researchers, big data engineers often have advanced degrees in mathematics and statistics. These degrees are necessary for designing, maintaining and building data pipelines based on massive data sets. Check out these top data architect and data engineer certifications.

 4. Business intelligence developer

 Business intelligence (BI) is also a data-driven discipline that predates the modern AI rush. Like big data and AI, BI also relies on large data sets. BI developers use data analytics platforms, reporting tools and visualization techniques to turn raw data into meaningful insights to help organizations make informed decisions. BI developers work with a variety of coding languages and tools from major vendors, including SQL, Python, Tableau from Salesforce and Power BI from Microsoft. They also need to have a strong understanding of business processes to help improve them through data insight.

 5. Computer vision engineer

 A computer vision engineer is a developer who specializes in writing programs that utilize visual input sensors, algorithms and systems. These systems see the world around them and act accordingly, such as self-driving and self-parking cars and facial recognition. They use languages like C++ and Python, along with visual sensors, such as Mobileye from Intel. Examples of use cases include object detection, image segmentation, facial recognition, gesture recognition and scenery understanding.

6. Data scientist

A data scientist is a technology professional who collects, analyzes and interprets data to solve problems and drive decision-making within the organization. They are not necessarily programmers, although many do write their own applications. Mostly, they use data mining, big data and analytical tools. Their use of business insights derived from data enables businesses to improve sales and operations; make better decisions; and develop new products, services and policies. They use predictive modeling to forecast future events, such as customer churn, and data visualization to display research results visually. Some also use machine learning to build models to automate these tasks.