How do I choose a career in AI?
In this article, we’ll learn about the various careers in AI and what each entails.
There are many career paths that involve working with AI.
Some of these career paths use AI tools to be more productive with daily tasks.
Others involve building AI applications and automating manual tasks with AI.
Still, other career paths involve advancing the state of the art in AI research.
So, to help you choose the career path that is right for you, here are some of the top careers in AI.
An AI tool user is someone who uses AI-powered tools to assist them in performing their day-to-day tasks. This could be a software developer using AI to help them write better code, an author using AI to assist in their writing process, or an artist using AI to help create new digital art. AI tool users are significantly more productive than human-only or computer-only users — which provides huge advantages.
To become an AI tool user, you need basic AI literacy, experience in your domain of expertise, and training for each specific AI-powered tool you plan to use. For example, Github Copilot for a software developer, ChatGPT for an author, or DALL-E 2 for a digital artist. This option is the quickest, easiest, and lowest cost of all of the AI career paths available. It’s a great way to quickly upgrade your existing career for AI.
An AI Developer creates and maintains software that allows machines to perform tasks that typically require human intelligence. For example, performing tasks that involve natural language processing, image recognition, and text generation. AI Developers use pre-built AI services, customize pre-trained ML models with transfer learning, train new ML models, and implement AI algorithms from scratch.
To become an AI developer, you will likely need a 4-year degree in Computer Science (CS), Data Science (DS), software engineering, or a related field. If you are already an experienced software developer, you can learn the necessary skills through online courses. You will need to learn how to use 3rd-party AI services, customize pre-trained ML models, train ML models, implement AI algorithms, and build AI applications.
A machine learning specialist creates ML models to make decisions or predictions. They are responsible for training ML models using ML training algorithms. They perform data collection, preprocessing, and feature engineering. They also train, validate, test, deploy, and monitor ML models. An ML specialist also ensures that models are scalable, efficient, and used responsibly and ethically.
To become an ML specialist, you will typically need a 4-year degree in computer science, data science, or a related engineering field. If you are already an experienced IT professional, you can learn the necessary skills through online courses. You will need to know how to collect, clean, transform, and organize data. You will also need to know how to train, test, customize, deploy, and maintain ML models.
A robotics engineer creates robots that can perform various tasks autonomously. They are responsible for designing, building, programming, and testing robots that can perform a variety of functions, from manufacturing and assembly to exploration and medical procedures. Robotics engineers may also be responsible for monitoring, maintaining, and using these robotic systems.
To become a robotics engineer, you will need (at minimum) a 4-year degree in robotics, mechanical engineering, or a related engineering field. You will likely also need to be licensed as a professional engineer (PE). You will need to know how to build robotic systems, program them, train ML models to perform specific tasks, verify system performance, and maintain these robotic systems.
An AI project manager oversees the implementation of AI projects. They are responsible for managing the entire project lifecycle, from planning and design to execution and delivery. They work closely with a team of software developers, engineers, data scientists, and other stakeholders to ensure the project is completed on time, within budget, and meets the required quality standards.
To become an AI project manager, you will likely need a 4-year degree in business administration, management, or a related field. In addition to project management skills and basic AI literacy, you will also need to understand the AI project life cycle and the pros and cons of various AI tools. AI projects are highly iterative, experimental, and not clearly defined. They are very different from traditional projects.
An AI consultant advises organizations on how to incorporate AI into their business operations and strategy. They work with clients to understand their business needs, then they develop and implement solutions that leverage AI to improve efficiency, reduce costs, and gain competitive advantage. They may also advise executives, train employees, oversee teams, and assist with the ethical use of AI.
To become an AI consultant, you will need (at minimum) a 4-year degree in CS, DS, or a related field. As a consultant, organizations are looking for you to be an expert on AI. So, you need to be very knowledgeable about all aspects of AI and business consulting. You will be expected to offer advice, educate employees, oversee AI projects, and develop a culture of AI within these organizations.
A data scientist uses data science and statistics to analyze and interpret data. They are responsible for collecting, cleaning, transforming, and organizing large sets of data. They also analyze, visualize, and model these data to uncover insights and trends that can inform business decisions or solve real-world problems. They may also work with Big Data, create ML models, and deploy AI tools into production.
To become a data scientist, you need a 4-year degree in data science, statistics, or a related field. In addition, most data scientists also have a Master’s or Ph.D. in data science or a similar field. As a data scientist, you must understand all aspects of the data science lifecycle, including running experiments, collecting data, analyzing data, building models, and communicating results to a broader audience.
An AI research scientist conducts research to advance the field of artificial intelligence. They are responsible for developing new algorithms, ML models, and techniques that can improve the performance and capabilities of AI systems. They also run experiments to test hypotheses, analyze and interpret data, collaborate with other researchers, and publish their findings in scientific journals.
To become a research scientist in AI, you will need to acquire a Ph.D. in Computer Science, Data Science, Cognitive Neuroscience (CNS), or a related field. To perform AI research, you will need to understand various aspects of the scientific method, research methods, the peer-review process, and publishing research in academic journals.
There are many other careers that work within the AI value stream. Some of these careers include:
To learn more, please read the next article in this series: Choosing a Degree in AI.