Artificial intelligence (AI) is the development and training of computers to simulate and perform tasks that usually require human intelligence. In short, the goal of AI is to get machines (e.g. computers) to act like a person with human-like intelligence. And as you know, since the launch of OpenAI’s ChatGPT in 2022, companies and organizations of all sizes are clamoring to identify ways to use AI applications to make their organizations more efficient, effective, and profitable. But who makes artificial intelligence possible? The answer to that question is: artificial intelligence engineers, a sub-set of software engineers who specifically focus on building machine learning algorithms using their programming, machine learning, data science and data analysis, neural networks, and other technical and soft skills.
The U.S. Bureau of Labor Statistics projects that computer and information technology (IT) jobs will grow faster than all other jobs from 2022 and 2032, and luckily for you, AI falls in that category. In addition, artificial intelligence skills are some of the most in-demand among employers—according to CompTia, over 10% of tech job listings are hiring for AI related skills.
But how do you make sure you land a job working in AI? Beyond education, you need to work on your skills — skills employers use to set you apart from other tech professionals.
So let’s get started. Are you interested in breaking into AI? Keep reading to learn about the essential skills you need to work in AI, the types of jobs you can land with them, and which companies are actively looking for AI-skilled employees.
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Hard Skills for AI Engineers
What’s one plus one? If you answered two with a bit of an eye roll or feelings of skepticism, don’t worry — I have a point. Knowing the answer means you have basic — albeit very basic — hard skills in math. And what are hard skills? Hard skills are learned through education or experience. They are technical skills or abilities that can be measured. And for the top skills in AI, you may need to demonstrate these hard skills through a combination of materials — degrees, certifications, testing, or a portfolio of AI projects.
Skill 1: Computer Programming
Computer programming is the top skill in AI. This sounds like a fact, but it’s technically an opinion. If you ask me — which you are — programming skills and languages are central to working with AI. How can you create, rework, or test AI if you don’t know how to program a computer to act like a human? Every job in AI won’t require masterful skills in programming, but the majority will, so it’s best to get ahead of the curve.
AI software development calls for knowing a number of different programming languages. The most common programming language — and the one employers desire the most — for building AI is Python. Because of its simplicity and ease of use, Python is beginner-friendly, but its scalability and versatility make it a staple for even experienced programmers.
The full list of programming languages you should consider mastering for AI includes:
R
Lisp
Haskell
Julia
Prolog
Scala
📌 Related: Interested in learning more Python? Check out our complete guide on how to learn Python.
Skill 2: Machine Learning
AI and machine learning are often used interchangeably, but they’re not actually the same thing. From the name, we know that AI is artificial intelligence meant to copy the way humans think. And as humans, we can learn, evaluate, and reason. Machine learning is an AI skill that uses data analysis— or algorithms — to create AI systems that can perform some of our human functions.
For example, imagine you tell your friend that you like K-pop. Using this, they recommend the South Korean global sensation, BTS. Why? They learned about your likes (K-pop music), evaluated the information, and reasoned that you’d probably like BTS, but I mean, who doesn’t? In this case, you could consider your friend like Spotify or Apple Music who use machine learning to automate making musical suggestions.
So, to be technical, you wouldn’t say that AI and machine learning are one. Instead, AI uses machine learning. The most popular machine learning frameworks you can learn to boost your skills in AI include:
TensorFlow
PyTorch
Keras
Skill 3: Data Science
Data science is the study of data to organize, analyze, and interpret information. It’s a cycle that includes acquisition (capturing data), warehousing (maintaining data), mining (processing data), exploration and confirmation (analyzing data), and reporting (communicating data). The reason why data science and data analysis are some of the top skills required for AI engineers is because it shows employers that you can take tons of data and make it make sense.
Applications, tools, or machines that use AI, use algorithms to carry out different tasks. And the only way they know how to do these tasks is by relying on large data sets to learn what common patterns of association are. With data science skills, you can analyze data and develop algorithms so companies and AI technologies can use the information to train their systems.
Some skills, frameworks, and technologies you’ll want to learn to work with large quantities of data include:
Mathematical skills including linear algebra, linear regression, and statistics
Big data technologies such as Apache Hadoop and Apache Spark
Deep learning (dpl), natural language processing (nlp), and speech recognition
Working with APIs
Data visualization
📌 Related: Interested in becoming a Data Scientist? Check out our guide.
Skill 4: Neural Networks
Can you teach a computer to think like the human brain? If you can, this is another AI skill employers will drool over. Neural networks are an AI framework that teaches machines to copy the learning and thinking patterns of the human brain. The definition is simple, but the work behind it is extensive. Instead of getting too technical with terms like perceptrons, inputs, outputs, and biases, let’s use handwriting recognition as an example.
Imagine you’re writing on an iPad and using a “handwriting to text” feature. In the beginning, you’re using print before quickly switching to cursive. And even with this change from print to cursive, the technology still accurately identifies what you’re writing. How? When building the neural network, the AI engineer likely used hundreds — if not millions — of training examples so the system could learn more about handwriting. With these examples built into the system, it makes your iPad more accurate at recognizing letters, numbers, and symbols.
Skill 5: Soft Skills
Most of us worry about filling our resume with hard skills. We comb through our education and experience to pinpoint what we’ve learned. And while employers are interested in these skills, that’s not all. They’re also looking for AI skills that you can’t test with paper and pencil — or in this case, with a computer.
Soft skills are non-technical, interpersonal skills that affect how you do your job. They dictate how you interact with your peers and your work. Soft skills boost how you use and integrate your hard skills. What good is it to know Python if you don’t have the critical thinking skills to troubleshoot its issues?
Some of the best soft skills for AI jobs include:
Critical thinking and problem solving: The ability to learn, analyze, interpret, and apply information to identify connections and make reasonable judgments.
Networking: The process of sharing information and exchanging ideas with people to create new relationships or maintain existing ones.
Emotional intelligence: The ability to identify, understand, and manage your emotions while recognizing the effect they have on you and how they affect others around you.
Intellectual curiosity: The desire to learn and explore new things.
Communication skills: The process of exchanging information, opinions, and thoughts through written, verbal, or non-verbal ways.
Let me let you in on a little secret. While all of these AI skills are important for a career in the field, pay special attention to emotional intelligence. Why? Remember that the purpose of AI is to mimic humans, and while we can pick up on different cues like changes in facial expressions, voice, and body language, AI inherently can’t. It would be up to you — if you’re in AI development — to build your AI to recognize these human traits.
Bonus Skill: Domain Knowledge
If you can claim a series of the above skills on your resume, you’re in an ideal spot for a career in AI — and tech, in general. But what if you could get an extra edge over your competition? It won’t be necessary for a lot of employers, but having domain knowledge could give you a leg up over your peers.
For example, if you’re applying for a role as an AI engineer within a healthcare company and have real-world experience in healthcare, make it known. With your knowledge, you could provide more insight than someone who knows nothing about healthcare.
And if you don’t have domain knowledge, don’t panic. Reading a few research articles or brushing up on industry trends can give you enough information to slyly drop in a conversation or interview.
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Common Jobs for a Career in AI Engineering
Now you know the top skills required for AI. What’s next? The ultimate goal is finding a well-paying job that suits the skills you have — and the ones you’re working on. The good news is that there are dozens of positions in the AI/tech industry and a number of different career paths you can take; however, there are common ones you’ll see pop up time and time again. And if the facial recognition and predictive text features of iPhones have shown me anything, it’s to work smarter, not harder. So instead of describing them myself, here’s a list of common jobs in AI — including responsibilities and required/desired qualifications or skills — as described by job posts on Indeed.
AI Research Scientist
This job listing is for the position of AI Research Scientist with the technology company, Meta.
Responsibilities:
Help advance the science and technology of intelligent machines
Devise better data-driven models of human behavior
Collaborate with other researchers and engineers across machine perception teams at Meta to develop experiments, prototypes, and concepts that advance the state-of-the-art in AR/VR systems
Work with the team to help design, setup, and run practical experiments and prototype systems related to large-scale long-duration sensing and machine reasoning
Contribute to research that enables learning the semantics of data (images, video, text, audio, and other modalities)
Qualifications or Desired Skills:
Experience in developing and debugging in C/C++, Python, or C#
Experience holding a faculty, industry, or government researcher position in a role with a primary emphasis on AI research
Experience in real-time computer graphics or modern GPU programming (CUDA, OpenGL, OpenCL)
Experience with designing (products or open-source) software for inertial/optical/wireless sensing devices
Experience showing first-author publications at peer-reviewed AI conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, and ACL)
This job listing is for the position of Data Scientist with the retail corporation, CVS Pharmacy.
Responsibilities:
Develops, validates, and executes algorithms and predictive models to investigate problems, detect patterns, and recommend solutions.
Explores, examines, and interprets large volumes of data in various forms.
Performs analyses of structured and unstructured data to solve moderately complex business problems. utilizing advanced statistical techniques and mathematical analyses.
Develops data structures and pipelines to organize, collect, and standardize data that helps generate insights and addresses reporting needs.
Uses data visualization techniques to effectively communicate analytical results and support business decisions.
Qualifications or Skills:
Bachelor’s degree and 2+ years of work experience or Graduate degree and 1+ years of work experience
Extensive experience with SQL, Python, Spark & Cloud computing (AWS, GCP, MS Azure).
Demonstrates strong ability to communicate technical concepts and implications to business partners
Demonstrates proficiency in most areas of mathematical analysis methods, machine learning, statistical analyses, experiment design, and predictive modeling and in-depth specialization in some areas
This job listing is for the position of Machine Learning Engineer with the exercise equipment and media company, Peloton.
Responsibilities:
Build and improve ML pipelines that power Peloton’s content recommendations.
Research and apply best-in-class machine learning techniques for recommender systems.
Evaluate, implement, and improve machine learning models.
Run A/B tests and experiments and analyze the results in collaboration with our product analysts.
Productionize, deploy, and monitor machine learning models and services.
Collaborate and work closely with our platform teams to leverage their tools and infrastructure to rapidly iterate on ideas that drive delightful personalized experiences for millions of users.
Qualifications or Desired Skills:
Degree in highly quantitative fields including Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.
2 Years of Experience in Machine Learning
Experience writing code in Python, Java, Kotlin, Go, C/C++ with documentation for reproducibility
Experience/Interest working in at least one of the following ML disciplines: recommender systems, natural language processing, or computer vision.
Solid understanding of software engineering principles and fundamentals including data structures and algorithms
Experience with relational and non-relational databases such as Postgres, MySQL, Cassandra, or DynamoDB
Software Engineer
This job listing is for the position of Senior Ops Generative AI Software Engineer at the banking company, Citi.
Responsibilities:
Contribute to both engineering and research
Develop foundational components and mature technology capabilities in AI and LLMs
Take a product-focused approach and build solutions that are robust, scalable, and easy to use
Work in a fast-paced environment tackling cutting-edge problems by constantly testing and learning
Pair programming for our products, be lean in your approach, and remove bureaucracy where you see it
Desired Skills:
Fluency in at least two programming languages, with a preference for Python, Javascript/Typescript, Golang
Experience designing control and sandboxing systems for AI research
PyTorch, TensorFlow experience
Deep hands-on knowledge of Kubernetes, developing backend platforms and engineering solutions that scale
Direct engineering experience of high performance, large-scale AI/ML systems
📌 Related: Wondering about the difference between Web Developers vs. Software Engineers? Check out our blog post that explains exactly that!
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What Companies Are Hiring AI Engineers?
The capabilities of artificial intelligence are endless. At the risk of sounding repetitive, AI is meant to mimic and copy people. So while it sounds alarming for some, wherever you see people working, there is likely a demand for AI. And it’s not necessarily to take jobs away from the workforce, which many can argue. It’s just that we can’t pretend that AI doesn’t increase efficiency, improve customer service, reduce human error, and handle repetitive tasks that we don’t want to do.
The need for AI is dominating most industries, but a few stand out. AI is especially prominent in:
Technology
Healthcare
Retail
Transportation and Travel
Finance
Education
Entertainment and Gaming
Companies that range from startups to the largest companies in the world are actively looking to fill AI positions. They include:
Adobe
Amazon
Apple
Google
Grammarly
Hive
IBM
Intel
Meta
Microsoft
NVIDIA
Samsung
Uber
Wells Fargo
AI isn’t going anywhere. Instead, you can expect the demand for the technology to grow. That means positive effects on your job hunt. In their Global Artificial Intelligence Market Report, International Trade Administration noted that, “In 2021, AI global funding doubled to $66.8 billion,” and you can expect more companies and governments — both domestic and international — to continue adopting AI solutions.
How to Start Learning AI Engineering Skills
Your knowledge is anything but artificial. You’ve likely put tons of work into educating yourself and honing your skills for a career as a developer. However, there’s always room to grow. If you’re missing an AI skill on this list, never fear! These skills are absolutely learnable, and you don’t need to go back to school for a master’s degree, computer science degree, or even sign up for a high priced coding bootcamp!
If anything, it might be a sign to check out the Skillcrush Break Into Tech – Full Stack Developer Career Track. It is an online course that teaches quintessential AI programming languages like Python and JavaScript. At the end of your program, you’ll get the support of resume reviews, mock interviews, and career counseling sessions to help you land a job in the growing field of AI.
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