Artificial Intelligence Software Engineer
Sanna Pospicil
Artificial intelligence (AI) is a branch of computer science that involves programming machines to think like human brains. While simulating human actions might sound like the stuff of science fiction novels, it is actually a tool that enables us to rethink how we use, analyze, and integrate information to improve business decisions. AI has great potential when applied to finance, national security, health care, criminal justice, and transportation [1].
For an AI engineer, that means plenty of growth potential and a healthy salary to match. Read on to learn more about what an AI engineer does, how much they earn, and how to get started. Afterward, if you're interested in pursuing a career as an AI engineer, consider enrolling in IBM's AI Engineering Professional Certificate to learn job-relevant skills in as little as two months.
Artificial intelligence engineers are individuals who use AI and machine learning techniques to develop applications and systems that can help organizations increase efficiency, cut costs, increase profits, and make better business decisions.
The course AI for Everyone breaks down artificial intelligence to be accessible for those who might not need to understand the technical side of AI. If you want a crash course in the fundamentals, this class can help you understand key concepts and spot opportunities to apply AI in your organization.
According to Glassdoor, the average salary for an AI engineer is $115,623 in the United States as of March 2024[3]. Falling under the categories of Computer and Information Research Scientist, AI engineers have a median salary of $136,620, according to the US Bureau of Labor Statistics (BLS) [4].
AI engineers typically work for tech companies like Google, IBM, and Meta, among others, helping them to improve their products, software, operations, and delivery. More and more, they may also be employed in government and research facilities that work to improve public services.
Not sure where to start? Consider enrolling in the University of Michigan's Python for Everybody Specialization to learn how to program and analyze data with Python in just two months. To learn the basics of machine learning, meanwhile, consider enrolling in Stanford and DeepLearning.AI's Machine Learning Specialization.
AI engineering is the process of combining systems engineering principles, software engineering, computer science, and human-centered design to create intelligent systems that can complete certain tasks or reach certain goals.
To better explain AI engineering, it is important to discuss AI engineers, or some of the people behind making intelligent machines. AI engineers work with large volumes of data to create intelligent machines. Sophisticated algorithms help businesses in all industries including banking, transportation, healthcare, and entertainment. AI is the disruptive technology behind virtual assistants, streaming services, automated driving, and critical diagnoses in medical centers.
AI engineers have a key role in industries since they have valuable data that can guide companies to success. The finance industry uses AI to detect fraud and the healthcare industry uses AI for drug discovery. The manufacturing industry uses AI to reshape the supply chain and enterprises use it to reduce environmental impacts and make better predictions. AI engineers provide essential solutions.
Other general skills help AI engineers reach success like effective communication skills, leadership abilities, and knowledge of other technology. Other disruptive technologies AI engineers can work with are blockchain, the cloud, the internet of things, and cybersecurity. Companies value engineers who understand business models and contribute to reaching business goals too. After all, with the proper training and experience, AI engineers can advance to senior positions and even C-suite-level roles.
Becoming an AI engineer is a challenging task. Besides having deep computer science knowledge, an AI engineer must be adaptable and valuable to a company. Here are some tips that can guide you toward your goal:
Honing your technical skills is extremely critical if you want to become an artificial intelligence engineer. Programming, software development life cycle, modularity, and statistics and mathematics are some of the more important skills to focus on while obtaining a degree. Furthermore, essential technological skills in big data and cloud services are also helpful.
Finding tech job positions means following tech companies. While many tech companies are located in the United States, there are many large companies located all over the world. Additionally, tech startups are a global movement. That means you can find an AI engineering position in most countries. Nevertheless, the United States has a large amount of AI engineering positions.
Continuing your education is helpful in every industry. Taking courses in digital transformation, disruptive technology, leadership and innovation, high-impact solutions, and cultural awareness can help you further your career as an AI engineer.
Ai is the moving force behind machine learning, the internet of things, cloud services, and cybersecurity. Learning about these disruptive technologies helps you reach your career goals. MIT Professional Education offers courses in technology, sustainability, leadership & innovation, and high-impact solutions that can help you reach your career goals in AI engineering and beyond. Some courses from MIT Professional Education that can boost technology roles include:
Artificial Intelligence Engineering is a branch of engineering focused on designing, developing, and managing systems that integrate artificial intelligence (AI) technologies. This discipline encompasses the methods, tools, and frameworks necessary to implement AI solutions effectively within various industries.
AI engineers work on creating algorithms, building advanced data processing techniques, and improving the robustness and performance of AI systems, ensuring they can solve complex problems, automate processes, and optimize operations efficiently. Their role is critical in bridging the gap between theoretical AI developments and practical, real-world applications, ensuring AI systems are scalable, sustainable, and ethically aligned with societal norms and business needs.
An AI engineer builds AI models using machine learning algorithms and deep learning neural networks to draw business insights, which can be used to make business decisions that affect the entire organization. These engineers also create weak or strong AIs, depending on what goals they want to achieve. AI engineers have a sound understanding of programming, software engineering, and data science. They use different tools and techniques so they can process data, as well as develop and maintain AI systems.
AI Engineers build different types of AI applications, such as contextual advertising based on sentiment analysis, visual identification or perception and language translation. The next section of How to become an AI Engineer focuses on the responsibilities of an AI engineer.
The first need to fulfill in order to enter the field of artificial intelligence engineering is to get a high school diploma with a specialization in a scientific discipline, such as chemistry, physics, or mathematics. You can also include statistics among your foundational disciplines in your schooling. If you leave high school with a strong background in scientific subjects, you'll have a solid foundation from which to build your subsequent learning.
You can enroll in a Bachelor of Science (B.Sc.) program that lasts for three years instead of a Bachelor of Technology (B.Tech.) program that lasts for four years. It is also possible to get an engineering degree in a conceptually comparable field, such as information technology or computer science, and then specialize in artificial intelligence alongside data science and machine learning. To get into prestigious engineering institutions like NITs, IITs, and IIITs, you may need to do well on the Joint Entrance Examination (JEE).
To give yourself a competing chance for AI engineering careers and increase your earning capacity, you may consider getting Artificial Intelligence Engineer Master's degree in a similar discipline. It might provide you with a comprehensive understanding of the topic as well as specialized technical abilities.
On the other hand, participating in Artificial Intelligence Courses or diploma programs may help you increase your abilities at a lower financial investment. There are graduate and post-graduate degrees available in artificial intelligence and machine learning that you may pursue.
To become an AI engineer, it's important to acquire practical experience. While a strong foundation in mathematics, statistics, and computer science is essential, hands-on experience with real-world problems is equally important. Through projects, and participation in hackathons, you can develop practical skills and gain experience with a variety of tools and technologies used in the field of AI engineering. Additionally, online courses and bootcamps can provide structured learning and mentorship, giving you the opportunity to work on real-world projects and receive feedback from industry professionals. With a combination of theoretical knowledge and practical experience, you can become a skilled AI engineer and contribute to the growing field of artificial intelligence.
Artificial Intelligence (AI) engineering is implemented across various sectors, transforming traditional operations and enabling new capabilities. Here are some real-world examples of AI engineering applications:
The discipline of AI engineering is still relatively new, but it has the potential to open up a wealth of employment doors in the years to come. A bachelor's degree in a relevant subject, such as information technology, computer engineering, statistics, or data science, is the very minimum needed for entry into the area of artificial intelligence engineering.
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