Artificial intelligence (AI) is one of the most transformative technologies of our time. Although it will replace some jobs and positions, it’s also creating a growing demand for skilled artificial intelligence programmers or AI programmers. AI is already being used in a wide range of industries, from healthcare and finance to manufacturing and retail. As AI becomes more sophisticated and widespread, the need for AI programmers will only continue to increase.
Understanding AI Programming
Okay, so if this is your first time hearing about this role, you may be asking, what is AI programming? Well, it’s the process of creating intelligent digital systems that can learn, reason, and act autonomously.
Artificial intelligence programmers use various techniques, including machine learning, deep learning, and natural language processing, to develop AI systems that can solve real-world problems. It's able to do this by:
- Learning from data: AI systems are trained on massive datasets of information. This data can be anything from text and images to code and sensor readings. By analyzing this data, the AI learns to identify patterns and relationships.
- Make informed predictions: Once trained, AI systems can be used to make predictions about future events. For example, an AI system might be able to predict what products a customer is likely to buy or whether a piece of equipment is about to fail.
And if the next question you have is, well, does that mean I need to learn an entirely new programming language to take this career path? Well, while there are some languages specifically designed for AI, many general-purpose languages like Python and Java are commonly used with AI libraries and frameworks. The answer is no; however, you will have to spend time learning other aspects of AI development.
Learning the Basics
A strong foundation in programming languages, mathematics, and computer science fundamentals is needed to succeed as an AI engineer.
The good news for those who have clocked in many years of experience in software development is that some of the most important AI programming languages are ones that you may already be using or have used in the past, such as Python, C++, Java, and R.
For young aspiring programmers, you’ll also need to be familiar with linear algebra, calculus, and statistics, in addition to understanding how certain programming languages are applied to AI development.
Most software engineers have pursued advanced degrees in computer science or mathematics or have taken advanced courses to earn certifications. And while formal education is not a requirement, it levels up your career opportunities to do so.
Thanks to this gift called the internet, which we love to hate, there are many online resources available to help you learn the basics of AI programming. Courses and tutorials can be found on Coursera and Google, which are great places to get your feet wet with AI programming to understand if it’s a career path that you want to pursue.
Once you have a basic understanding of AI programming, start practicing your coding skills. You can’t get better if you don’t practice, and there are many ways to do this, such as working on personal projects, contributing to open-source projects, or participating in AI competitions.
Regardless of your experience in software development, it’s always beneficial to either shadow a peer who is currently programming AI or even participate in hackathons. Participating in real-world applications and situations can give you a great deal of understanding of computer programming.
Specializing in AI: What Does it Look Like?
Artificial intelligence is being applied in nearly any way that can bring impact and efficiency. So naturally, many different subfields of AI have developed, each with its unique focus.
You don’t have to stick to a particular subfield or even industry. Artificial intelligence engineers are sought after for a multitude of reasons. Some of the most popular AI subfields include:
- Machine learning is a type of AI that allows systems to learn from data without being explicitly programmed. Machine learning is used in a wide range of applications, such as spam filtering, product recommendation systems, and fraud detection.
- Neural networks are machine learning algorithms that are inspired by the human brain. Neural networks are used in a wide range of applications, such as image recognition, natural language processing, and machine translation.
- Natural language processing is a field of computer science that deals with the interaction between computers and human (natural) languages. Natural language processing is used in various applications, such as machine translation, chatbots, and text summarization.
- Computer vision deals with the ability of computers to understand and process images and videos. Computer vision is used in a wide range of applications, such as facial recognition, self-driving cars, and medical imaging.
As you learn more about AI programming and find what interests you the most, you may gravitate toward specializing in a particular subfield. This will allow you to develop deeper expertise in a specific area of AI.
However, software and data engineering roles are increasingly required to have some knowledge of artificial intelligence. About 39% of companies have recruited software engineers for their skills and knowledge in AI, and 91% of the top organizations say they plan to invest in more AI activities, increasing demand for AI programming roles.
Stay Current, Know What’s Going On
This career path is one of continuous learning. Nothing will stay the same forever, and new programming languages will have their purpose just as the older programming languages do. New tech will come, and old tech will hang on for dear life.
But if AI programming is a career path you would like to take, having a curious mindset will allow you to keep up with the changes happening around you as well as elevate your problem-solving and critical thinking skills.
Read AI blogs and articles, attend AI conferences and meetups, listen to podcasts, or even watch YouTube; anything that will help keep you current on the shifts within technology and the industries you may work in will give you additional knowledge to improve your skills in AI programming.
Showcase Your Work by Building a Portfolio
Yes, even software developers should have a portfolio, and it’s time to start building one and crafting a resume that displays your skills once you’ve gained some experience in AI programming. Your portfolio should showcase your soft skills, AI skills and demonstrate your ability to solve real-world problems. Not to mention, a well-crafted portfolio will help you stand out among other applicants.
Here are a few tips for building a strong AI portfolio:
- Include a variety of projects that will demonstrate your breadth of AI knowledge, use of AI tools and skills.
- Highlight your contributions to help potential employers understand your added value in each project
- Use clear and concise language. Your portfolio should be easy to read and understand.
Creating a portfolio takes time and effort, but it’s worth it to create one and keep it updated throughout time. Think of your portfolio as a living document. You’re never finished with it; it’ll always change and grow as you gain more experience. When it’s time to apply for AI programming roles, you’ll thank yourself later.
What Job Opportunities Are Available for AI Programmers?
Since AI is expanding to every industry you can imagine, you aren’t stuck only with opportunities in the tech; healthcare, retail, manufacturing, finance, education, government, and the video game sector are all hiring more AI-led roles.
With experience working in different industries, you open yourself to learning more about real-life AI applications and the challenges that are often met. Having the versatility to move between multiple industries also increases your earning potential and broadens your experience skills in AI programming. A few roles you may be interested in include:
- Machine learning engineer: Focus on building and implementing the machine learning models that form the core of AI systems
- Data scientist: Acts as the information architect, responsible for collecting, cleaning, and preparing the data that machine learning models train on.
- Software engineer: Build software infrastructure that supports the AI system. They write code to integrate the machine learning models.
- Natural language processing engineer: Specialize in building AI systems that can understand and process human language.
- Computer vision engineer: Develops AI systems that can interpret and analyze visual data.
AI programming roles are expected to grow 26% in the next seven years, which is good news for those who want to break into AI development.
Start Pursuing Your AI Career
If you are interested in a career in AI programming, many resources are available to help you get started. You can learn the basics of AI programming through online courses and tutorials and gain experience by working on personal projects or contributing to open source projects.
Becoming an AI programmer requires hard work and dedication, but it’s a rewarding career path that provides opportunities to improve your skills as a software engineer and work on cutting-edge technologies. If you’re looking for your next move in AI, and an opportunity to join the elite, send in your resume and follow us on LinkedIn for latest news and updates!
