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10 top AI jobs in 2023

The AI revolution is changing many jobs and creating some new ones. Learn the top skills, industries and jobs that AI is making its mark on.

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.

Top AI job skills

A career as an AI programmer means knowing the right languages. The most popular languages for AI development are Python, C++, Java and Compute Unified Device Architecture, which Nvidia developed for programming its GPUs. It is best to be well versed in multiple languages because various languages are used in different settings. For example, an edge application might be written in Java, but then, the data is sent down to a central server and is processed in Python.

However, AI programming requires considerably more skill than coding alone. These applications work on their own initiative or generate content, which requires a strong understanding of machine learning and statistical concepts.

AI jobs require critical thinking skills to solve problems and analyze user input. The same practices apply to code. Having strong mathematical skills can help people develop advanced algorithms for programs.

AI is also unique because it requires some knowledge of psychology. To create AI, people need to understand how humans think and how they might behave in different situations. AI simulates human behavior.

Learn more about new skills in demand as generative AI reshapes tech roles.

Industries hiring AI jobs

Some industries are embracing AI faster than others. These include the following:

  • Technology. Tech firms of all types are adding AI to their products to enhance their use and make them simpler and more user-friendly. Hyperscalers, such as Google, Amazon and Microsoft, are all actively hiring AI specialists to build services.
  • Finance. The finance industry is making broad use of AI with simple tasks, such as automation, and more advanced uses, such as improving risk management and making better investment recommendations and decisions.
  • Healthcare. The healthcare industry is also rapidly embracing AI at all levels. On the low end, AI is being used for automation to avoid human error and for tasks such as billing and record management. On the high end, AI is being widely touted for early detection of serious illnesses, such as cancer, because the AI may spot signs that humans might miss.
  • Retail. The retail industry is making wide use of AI for operational efficiency. AI can be used for areas such as inventory management, loss prevention, trend spotting, more personal shopping experiences and fraud prevention by finding suspicious spending patterns or transactions.
  • Manufacturing. The manufacturing industry is embracing AI for operational efficiency. AI can spot early detection of potential equipment failure and help machinery run efficiently.
  • Cybersecurity. The cybersecurity market is embracing AI to monitor around the clock and avoid human error. AI applications can be programmed to detect unusual activity quickly for swift action.

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.

7. Machine learning engineer

A machine learning engineer is responsible for developing and implementing machine learning training algorithms and models. Training is the demanding side of machine learning and is the most processor- and computation-intensive aspect of machine learning. Therefore, it requires the highest level of skill and training.

Because of the need for advanced math and statistics skills, most machine learning engineers have advanced degrees in computer science, math or statistics. They often continue training through certification programs or a master's degree in machine learning, deep learning or neural networks.

8. Natural language processing engineer

A natural language processing (NLP) engineer is a computer scientist who specializes in the development of algorithms and systems that understand and process natural human language input.

One of the big differentiators between traditional search engines and generative AI interfaces, such as ChatGPT, is that search engines use keywords and gather information from large amounts of existing online data. Generative AI creates new content based on other examples and patterns, and it answers queries in a chat-type format.

Like machine learning engineers, NLP engineers are not necessarily programmers first. They need to understand linguistics as much as they need to understand programming. NLP projects require machine translation, text summarization, answering questions and understanding context.

9. Robotics engineer

A robotics engineer is a developer who designs, develops and tests software for running and operating robots. Robotics has advanced significantly in recent years, such as automated home cleaners and precision cancer surgery equipment. Robotics engineers may also use AI and machine learning to boost a robotic system's performance.

As a result, robotics engineers are typically designing software that receives little to no human input but instead relies on sensory input. Therefore, a robotics engineer needs to debug the software and the hardware to make sure everything is functioning as it should.

Robotics engineers typically have degrees in engineering, such as electrical, electronic or mechanical engineering.

10. Software engineer

A software engineer can cover various activities in the software development chain, including design, development, testing and deployment. Engineering professionals are needed at all points of software development. The demands are so high that it's rare to find someone well versed in all of them. Most engineers tend to specialize in one discipline.

This applies to AI as well. AI software engineers design, develop, test and deploy machine learning, inference, NLP, sensory input, cybersecurity and intrusion detection apps.

Learn the differences between a network engineer and a software engineer.

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