Browse Definitions :
Democratization of AI creates benefits and challenges AI model optimization: How to do it and why it matters

10 top AI jobs in 2024

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.

If 2023 was the breakout year for artificial intelligence, then 2024 will be the year it starts to mature, with new jobs focusing on AI. This happens inevitably with every technological revolution -- technology takes off at a gallop and areas such as security and management must catch up.

In 2024, there will be a greater focus on data integrity, security and privacy. There will also be greater interest in the part of content creators on their data being used or not used for generative AI. In late 2023, The New York Times sued Microsoft and OpenAI over the use of Times' content in ChatGPT.

In addition, models are expected to both grow and shrink. They will grow as AI tries to become more comprehensive and helpful, but they will also shrink to become focused on one area of specificity.

They will also shrink because AI processing is moving off giant GPU farms and onto desktops and handheld devices. Intel, AMD and Qualcomm are all making processes with AI accelerators for desktop and laptop PCs. Inevitably, smartphone handsets will also be doing AI processing.

All this AI growth means more jobs. Below is a list of what might be the 10 hottest AI jobs and skills for 2024.

Top AI job skills

Being a successful AI developer requires more than just coding skills. Of course, proficiency in a core AI developer language such as Python, Java and R, along with emerging languages such as Julia or Scala, is essential.

AI jobs require knowledge of data modeling and engineering to structure and preprocess data for efficient AI training and analysis. Individuals also need to understand machine learning and deep learning, and be knowledgeable in various algorithms, model architectures and optimization techniques.

In addition to programming skills, individuals need to know libraries and frameworks such as TensorFlow, PyTorch, scikit-learn and scikit-image, and have a strong background in mathematics and statistics.

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.

Finally, with an emphasis on AI security, privacy and data integrity, individuals need to know the best practices behind security and ethics.

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 can 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. In 2024, specialists are more sought after than generalists. Deep knowledge of one aspect of AI is more valuable than shallow knowledge across many areas. 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.

The technological demands of this job are a little higher than most product manager positions. 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. 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 is 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. AI ethics specialist

As stated earlier, ethical use of data used in generating models is going to become a foremost concern in 2024. Dedicated specialists will be needed to ensure responsible development and deployment of AI. Companies might also look to add an AI ethics committee made up of employees with various experiences and specialties including lawyers, engineers, ethicists, public representatives and business strategists.

An AI ethics specialist will help develop ethical guidelines and policies for AI projects and complete ethical reviews of these projects. They might report any findings to the AI ethics committee. Skills needed for this position include critical thinking, effective communication and familiarity with AI frameworks and regulations.

4. Cybersecurity analyst with AI expertise

AI has found a home in cybersecurity, particularly in intrusion detection. However, the threat actors also use AI. This is a field where specialists are needed who are both fluent in cybersecurity and in the skill sets to use AI to combat things such as ransomware and intrusion detection.

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 such as 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 might 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. AI for healthcare specialist

If ever there was an industry that needed a bridge between the technological side and the professional side, it is healthcare. Technology can help doctors and patients alike in many ways, but it is also one of the most sensitive fields when it comes to data privacy.

AI offers several opportunities for helping the medical profession, such as diagnosing diseases and identifying the best treatment plans for patients with critical medical decisions. Another example of AI in healthcare is the AI-powered robotics in the operating room that assist with surgery.

AI jobs in healthcare require a deep understanding of medical conditions and terminology as much as it requires AI expertise.

Andy Patrizio is a technology journalist with almost 30 years' experience covering Silicon Valley who has worked for a variety of publications -- on staff or as a freelancer -- including Network World, InfoWorld, Business Insider, Ars Technica and InformationWeek.

Next Steps

 AI engineers: What they do and how to become one

Top artificial intelligence certifications and courses

Main types of artificial intelligence: Explained

 AI transparency: What is it and why do we need it?

The future of AI: What to expect in the next 5 years

Dig Deeper on Data analytics and AI

  • cloud security

    Cloud security, also known as 'cloud computing security,' is a set of policies, practices and controls deployed to protect ...

  • privacy impact assessment (PIA)

    A privacy impact assessment (PIA) is a method for identifying and assessing privacy risks throughout the development lifecycle of...

  • proof of concept (PoC) exploit

    A proof of concept (PoC) exploit is a nonharmful attack against a computer or network. PoC exploits are not meant to cause harm, ...

  • data collection

    Data collection is the process of gathering data for use in business decision-making, strategic planning, research and other ...

  • chief trust officer

    A chief trust officer (CTrO) in the IT industry is an executive job title given to the person responsible for building confidence...

  • green IT (green information technology)

    Green IT (green information technology) is the practice of creating and using environmentally sustainable computing resources.

  • diversity, equity and inclusion (DEI)

    Diversity, equity and inclusion is a term used to describe policies and programs that promote the representation and ...

  • ADP Mobile Solutions

    ADP Mobile Solutions is a self-service mobile app that enables employees to access work records such as pay, schedules, timecards...

  • director of employee engagement

    Director of employee engagement is one of the job titles for a human resources (HR) manager who is responsible for an ...

Customer Experience
  • digital marketing

    Digital marketing is the promotion and marketing of goods and services to consumers through digital channels and electronic ...

  • contact center schedule adherence

    Contact center schedule adherence is a standard metric used in business contact centers to determine whether contact center ...

  • customer retention

    Customer retention is a metric that measures customer loyalty, or an organization's ability to retain customers over time.