Automation and AI technology develop at the speed of light and disrupt the job market. With all the hype surrounding AI-based software like chatbots, smart assistants, and generative AI tools like LLM and image synthesizers, AI engineers seem to be the rock stars of tomorrow. Yet how does one become an AI engineer? Is it enough to be interested in tech and hand in consistently good programming homework for your computer science class, or does it take something more?
Don’t worry, as it is with almost any profession under the sun, the secret ingredient to becoming an artificial intelligence engineer is unwavering motivation. If you have this, everything else is achievable. However, let’s look at what skills and qualifications you should develop to start a career in AI engineering.
What Is an AI Engineer?
As it obviously follows from the name, AI engineers are professionals dealing with AI, that is, Artificial Intelligence. AI refers to various specialized software systems designed to mimic certain aspects of human behavior: image processing, pattern recognition, natural language generation, etc.
An AI software engineer is responsible for designing, building, and maintaining AI-based systems and applications that perform complex tasks typically requiring human intelligence, for example, understanding natural language and responding accordingly, recognizing objects from images, solving problems, and making decisions. As of yet, AI systems aren’t as effective as humans, but they are productive. AI is tireless and can process large amounts of data very fast, so it’s an invaluable tool in various fields and industries: health care, marketing, business, research, and many others.
What Do AI Engineers Do?
AI engineers work at the intersection of computer science, data science, and machine learning. In the most general terms, they build AI systems using neural networks and machine learning algorithms to perform specific tasks depending on the industry’s or particular company’s needs. AI engineers develop, fine-tune, and maintain one of today’s most sophisticated tools.
Here are some of the roles that AI engineers might perform:
- Developing algorithms: Creating AI algorithms from scratch or fine-tuning existing models to solve specific problems (for example, diagnosing patients or analyzing emotions in audio recordings.)
- Preparing data: Collecting, refining, and organizing datasets intended for training AI models.
- Machine learning: Training AI models on datasets with the help of machine learning techniques.
- Deep learning: A subfield of machine learning that uses neural networks with multiple layers essential for complex human-like tasks like speech recognition and image processing.
- Programming: Developing AI applications using programming languages (e.g., Python, Java, C++).
- Deployment: Integrating AI solutions into existing applications (e.g., chatbots, autonomous vehicles, recommendation systems, etc.)
- Evaluating models: Assessing the performance of AI models and improving their efficiency to fit specific tasks.
- AI ethics: Ensuring AI systems are developed and used ethically, preventing biases and privacy violations.
What you will do as an AI engineer depends on the industry that requests your services. Sometimes, you will be required to combine some or all of those roles.
How Much Do AI Engineers Make?
This specialization is in demand and quite lucrative. The pay scale of an AI engineer is considerably higher than that of a software engineer or a data scientist, with entry-level AI engineers in the US making anywhere between $91,000 and $153,000 a year, with an average of $117,377 a year.
With more experience and a more illustrious CV, even higher remuneration can be expected. For example, intermediate-level AI engineers with 3 to 6 years of experience in the industry are likely to earn somewhere between $109,000 to $179,000 with an average of $138,000. Advanced AI engineers with 10+ years of experience can expect to earn from $126,000 to $203,000, with an average salary of $158,000 a year. These numbers are more or less consistent across all the states and vary the most from company to company, with giants like Google, Facebook, and Walmart Labs offering the highest-paying positions. Salary ranges also vary according to industries, with tech, finance, health care, and retail leading the way.
In other countries, AI engineers can expect to earn proportionately desirable salaries in local currencies, as it is one of the highest-paying occupations worldwide – second only to top executive roles, lawyers, and medical professionals.
Moreover, the projected growth for AI engineers is 21% for the estimated period between 2021 and 2031, while the average for all occupations is only 5%. Hiring perspectives are also impressive: LinkedIn’s 2020 Emerging Jobs Report shows that the number of AI specialists hired has grown 74% annually in recent years.
Of course, how much you can earn also depends on your academic credentials, technical skillset, and negotiating prowess.
How to Be an AI Engineer?
As it is with any other field, being a good AI software engineer requires a combination of theoretical knowledge, hands-on skills, and formal education. Since it is a dynamic and diverse field, your specific role might require specialized knowledge in other areas, for example, reinforcement learning, robotics, or computer vision, so you probably should prepare for continuous on-the-job training and life-long learning if you choose this career path. To begin working as an AI engineer, you will need to master the following skills:
- Knowledge of linear algebra, calculus, statistics, and probability theory.
- Programming languages – the more, the better. Start with Python, R, Java, Scala, TypeScript, and C++.
- Familiarity with algorithms for supervised and unsupervised learning, deep learning, and reinforcement learning.
- Understanding of deep learning frameworks like TensorFlow, PyTorch, and Keras.
- Proficiency in data manipulation and processing via libraries like Pandas and NumPy.
- Knowledge of data storage, retrieval, and manipulation, including work with big data technologies like Hadoop and Spark.
- Proficiency in software development practices, such as version control, testing, etc.
- You might also need knowledge pertaining to your specialization, for example, natural language processing (NLP) or computer vision, as well as other domain-specific knowledge, for example, knowledge of medical concepts and practices for developing AI systems for healthcare.
A Bachelor’s degree in a relevant field like computer science, artificial intelligence, machine learning, engineering, mathematics, statistics, etc., won’t go amiss. Yet, a Master’s or Ph.D. in the aforementioned fields are particularly desirable if you plan to work on a cutting-edge AI project and conduct in-depth research.
How to Become AI Engineer Without a Degree?
A formal degree significantly affects your attractiveness as a candidate and your prospective salary. On the other hand, the absence of a four-year degree should not preclude you from entering this profession! First, few (if any) colleges offer artificial intelligence engineer degree. Those who desire to pursue this career path usually settle for a degree in mathematics or computer science with only some courses relevant to AI and pick up the bulk of the skills independently. Here is how you can learn relevant skills to become an AI engineer without a college degree.
Bootcamps
This is a great way to learn multiple relevant skills faster and cheaper than a formal degree. Bootcamps offer flexible learning modes: you can choose them by specific course, instructor, or way of learning (group work vs. independent projects, online vs. face-to-face, etc.) Many boot camps also provide career advice or even guarantee employment after successful completion, making them an excellent entry point into the industry.
Certification
Professional certification is a great way to confirm your proficiency as an efficient self-learner and improve your hiring chances. Various AI engineer certification programs are available, including Professional Engineer certification from the National Society of Professional Engineers and AI Foundation Pathway from the Chartered Institute for IT. However, in some cases, there are minimum requirements to enter certification programs, such as prior experience in the industry, a series of examinations, or a higher education diploma.
Portfolio
The best way to impress potential employers if you don’t have a formal degree is through a portfolio of work. This might include work you’ve completed during courses and boot camps, internships, or passion projects you’ve worked on in your spare time. If you struggle to find suitable projects to showcase, consider gaining some experience, hone your skills, and develop competencies through internships or independent projects.
Internship
Internships offer excellent opportunities to get hands-on experience in a real-world environment, build a professional network, receive training, and show potential employers what you can do, sometimes even resulting in full-time job offers. In any case, an internship on your CV improves your chances of being hired. Look for internships that align with your existing skills and long-term career goals.
Mentorship
A mentor can be vital in any career, especially in an emerging profession like AI engineering. An expert with an established career in the field can help you focus on the crucial skills to learn and share valuable insights from their industry experience. They can boost your confidence by evaluating your strengths and guide you by pointing out areas of development.
Self-learning
Even if your employer does not require a formal degree, they will expect you to have a solid foundation in programming, data structures, and algorithms and be a strong problem-solver and critical thinker. Moreover, you must be willing to learn continuously and stay abreast of the latest developments in the field. So, rerun your eyes over the skills listed in the “How to be an AI engineer?” section and start planning your learning schedule with a pronounced focus on as many programming languages as possible.
Network
Finally, networking with other professionals and learners is beneficial since it allows you to access valuable insights, career advice, and opportunities from mentors, experts, and colleagues. Networking is crucial for staying current with emerging trends, learning best practices, gaining exposure to adjacent markets, and identifying new career opportunities.
The allure of the AI profession is undeniable, with rapid industry growth, high demand, and impressive paychecks. However, it all comes to those who are determined and disciplined enough to master many hard and soft skills through formal education or self-learning. Good luck to you on the path to the career of your dreams. Don’t forget that nothing is impossible!