Artificial Intelligence Job Description

Artificial intelligence (AI) continues to transform a wide variety of fields and industries, and it shows no sign of slowing, with an anticipated growth rate of 27.67 percent from 2025 through 2030 [1]. The technology has been particularly helpful in how companies conduct market research and perform data analysis. Other increasingly common AI applications include: 

• Chatbots

• Self-driving cars

• Voice-recognition smart assistants

• Disease mapping

• Automated social media monitoring

Employees fitting various AI job descriptions will likely continue to be in high demand in the future, and those possessing the right combination of education, skills, and hands-on experience can work in numerous fascinating sectors. AI adoption is particularly common in information technology (IT), education, real estate, finance, and health care. 

Learn more about what you might find in an artificial intelligence job description, various AI-based opportunities, and the skills you need to work as an AI professional as you plan your career.

What is artificial intelligence?

Broadly speaking, AI is a technological protocol that allows machines to simulate human tasks such as: 

• Learning

• Comprehension

• Creativity

• Decision-making

• Problem-solving

Programmers use modern AI to teach machines to perform highly sophisticated operations. Most commonly, you’ll encounter this sort of technology in the form of generative AI, a chatbot-like interface that produces textual, audio, and visual outputs in response to user prompts. 

AI, in large part, builds on a pair of related concepts: machine learning and deep learning. 

Machine learning (ML)

This subfield of AI teaches computers how to perform tasks in a human-like way via probabilistic learning. Programmers input data into a machine learning program, and as that program discovers data patterns, it learns to predict what data points will follow those it already knows in a way that resembles an autocorrect feature. Programmers then adjust the “weights” of specific answers to teach an ML program to make more accurate predictions. Ideally, ML allows AI to perform data analysis tasks automatically. 

Deep learning

Deep learning, another subfield of ML, is an AI training method based on certain principles of human learning. Deep learning protocols use neural networks to simulate human decision-making and teach AI models to learn from patterns in unstructured data. Deep learning operates without human intervention, allowing an AI model to essentially train itself. 

Combining these two learning methodologies results in today’s highly sophisticated AI models, including generative AI platforms like ChatGPT. 

Artificial intelligence job description details for various AI roles

Due to its myriad applications, you’ll find a broad range of specialized AI roles across sectors. Check out a few possible positions and some associated artificial intelligence job description details. 

AI research scientist

Average annual base salary: $102,316 [2]

AI research scientists often work in university environments. In this position, you will study and improve AI models. While, to a certain extent, you will work on the theoretical side of AI, you also have practical roles to play. As an AI research scientist, you will work with others to pioneer new ways to use the technology, performing tasks such as:

• Observing an AI platform’s output and refining its training process

• Discovering ways to streamline an organization’s use of AI

• Developing AI applications relevant to a business’s goals

• Developing the AI infrastructure a business uses

AI research scientists generally need a bachelor’s degree in computer science, mathematics, physics, or electrical engineering, along with coursework in algorithms, data structures, statistics, linear algebra, and calculus. You’ll need a solid foundation in AI ethics, research and analysis, and programming languages like R and Python. You'll also benefit from developing proficiency in statistical software and tools like Git, Jupyter Notebook, and LaTeX. 

Machine learning engineer

Average annual base salary: $123,600 [3] 

Machine learning engineers help companies turn enormous data stores into easily legible information they can act on to improve business metrics. Without ML professionals, this is a time-consuming, tedious process. Specific applications ML engineers help optimize include data mining and processing, robotics, and speech recognition.

ML engineers work in various fields, including entertainment, health care, manufacturing, and transportation. You will likely need a bachelor’s or master’s degree in computer science, data science, software engineering, or a related field. In addition to good communication skills, ML engineers must have knowledge of: 

• ML platforms such as Microsoft Azure, Amazon, IBM Watson, and Google Cloud

• Coding languages such as Java, Python, JavaScript, R, C, and C++

• Statistics

• Probability

• Data modeling

• Software engineering

Data scientist

Average annual base salary: $118,931 [4]

Data scientists utilize AI to extract useful information from extremely large data sets. This information allows them to help businesses streamline their operations and make better-informed data-driven decisions. Data scientists are in demand in finance, government, health care, telecommunications, and utilities. 

To enter the field, you’ll likely need a bachelor’s or master's degree in computer science or information technology. You might also consider boosting your credentials by completing boot camps or earning certifications, such as Microsoft Certified: Azure AI Fundamentals, or a vendor-neutral option like the Certified Analytics Professional (CAP) credential.

As a data scientist, you’ll need to have thorough knowledge of: 

• Languages such as Python, R, and Julia

• Mathematics

• Statistics

• Probability

AI software developer

Average annual base salary: $111,681 [5]

AI software developers use coding languages such as Python, C++, and Java to program software with AI capabilities. This software performs a variety of functions, such as: 

• Implementing and monitoring AI frameworks

• Teaching stakeholders to use AI

• Developing data transformation architecture

• Address business objectives

To become an AI software developer, you’ll likely need a bachelor’s degree in computer science, AI, engineering, robotics, or another related field. Some companies may require you to have a master’s degree, while others may accept self-paced training and certifications as an alternative.

In addition to specific education requirements, you will need proficiency in using tools such as: 

• Deep learning libraries and platforms

• Cloud platforms

• Google assistants

• IBM Watson

• Profiling tools

• Analytics tools

• Application programming interfaces (APIs)

Artificial intelligence duties and responsibilities

Developing models and algorithms and performing data analysis are among the chief AI duties you’ll likely find in artificial intelligence job descriptions. Explore each in more detail: 

• Developing AI models: You may work with all manner of AI applications, including those involved in speech recognition, data mining, and even advanced robotics. 

• Analyzing data: It’s important to monitor and analyze data correctly to keep projects on track. 

• Implementing algorithms to solve complex problems: You’ll need to know how to apply probability, logic, and ML algorithms to AI-related issues and think critically about how to resolve them. 

Essential artificial intelligence skills

AI skills are in high demand, which will likely continue as AI evolves and businesses adopt the technology. If you’re looking to work an AI-centric job, you’ll need to master a wide variety of essential technical and non-technical AI skills. 

Technical skills

Programming skills are essential for any AI worker. In addition to Python, you may also use C++, Java, and R frequently. You’ll also need to have extensive knowledge of ML frameworks such as PyTorch and TensorFlow, as well as a variety of ML and deep learning algorithms. 

Because AI interfaces are vulnerable to cyberattacks, it’s also beneficial to have knowledge of AI security measures. If your company plans to release a new AI model for general use, you’ll need to know about DevOps deployment techniques to ensure your AI model is usable in a general way. 

Non-technical skills

Because new developments in AI happen in real-time, you’ll need to be adaptable, no matter your precise job description. You’ll also need to be creative about how you use AI. Artificial intelligence isn’t a one-size-fits-all technology—only use it where it’s relevant and likely to boost business outcomes. 

Remember, however, that AI, no matter how advanced, can’t replicate innately human qualities such as emotional intelligence. No company functions well without workers who can communicate clearly, listen actively, and express empathy appropriately. Furthermore, human judgment is key when incorporating AI into workflows: Ethical considerations exist regarding data privacy, bias, and other issues with practical consequences. Human-derived AI governance can keep your AI-based workflow away from such pitfalls. 

Qualifications and experience requirements

Job qualifications and experience requirements depend on the type of AI-centric job you want. Most positions require at least a bachelor’s degree in a field such as: 

• Computer science

• Data science

• Electrical engineering

• Information technology

• Mathematics

• Physics

• Robotics

• Software engineering

To gain relevant experience with AI technology, it may be a good idea for you to look into internships or volunteer positions. These allow you hands-on experience with AI frameworks in numerous business contexts, which may significantly boost your resume. You can also explore boot camps and online courses to learn more about AI. 

Learn more about artificial intelligence with Coursera

AI professionals remain in demand in a variety of fields. The skills, education, and experience you need to land an AI-based job vary depending on what, exactly, your job description is.

Learn more about artificial intelligence with Coursera. Consider getting a thorough introduction with a course like DeepLearning.AI’s Generative AI for Everyone: Basics and Applications for All, which teaches the basics of AI development and practical usage for non-technical workers. To go deeper into the field, enroll in the IBM Machine Learning Professional Certificate for experience working with algorithms, training neural networks, and coding your own projects.