AI engineers are in high demand and for good reason. Artificial intelligence has seemingly endless potential to improve and simplify tasks commonly done by humans, including speech recognition, image processing, business process management, and even the diagnosis of disease. If you’re already technically inclined and have a background in software programming, you may want to consider a lucrative AI career and know about how to become an AI engineer.
What Is Artificial Intelligence?
Artificial Intelligence (AI) is a computer system’s ability to mimic human behavior. Machines demonstrate this sort of intelligence, which can be compared to a natural intelligence that humans and animals demonstrate.
In artificial intelligence (AI), machines learn from past data and actions, which are positive or negative. With this new information, the machine is able to make corrections to itself so that the problems don’t resurface, as well as make any necessary adjustments to handle new inputs. Finally, the machine is able to perform human-like tasks.
Let us understand what an AI engineer does in the next section of How to become an AI Engineer article.
Also Read: How Does Artificial Intelligence (AI) Work and Its Applications
What is AI Engineering?
Artificial intelligence engineering is based on the principles of systems engineering, software engineering, computer science, and human-centered design to create artificial intelligence systems that align with humans’ needs for achieving mission objectives.
What Does an AI Engineer Do?
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.
How To Get Into Artificial Intelligence
To become an expert in artificial intelligence, you will need to follow these steps:
1. Obtain a Secondary School Diploma
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.
2. Complete a Bachelor’s Degree
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).
3. Pursue a Master’s Degree
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.
4. Earn Certifications
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.
5. Acquire Practical Experience
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 Qualification/Education
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.
A master’s degree in artificial intelligence may be pursued after earning a bachelor’s degree in computer science. Having credentials in data science, deep learning, and machine learning may help you get a job and offer you a thorough grasp of essential subjects.
Responsibilities of an AI Engineer
As an AI engineer or an ML engineer, you need to perform certain tasks, such as develop, test, and deploy AI models through programming algorithms like random forest, logistic regression, linear regression, and so on. Responsibilities include:
- Convert the machine learning models into application program interfaces (APIs) so that other applications can use it
- Build AI models from scratch and help the different components of the organization (such as product managers and stakeholders) understand what results they gain from the model
- Build data ingestion and data transformation infrastructure
- Automate the infrastructure that the data science team uses
- Perform statistical analysis and tune the results so that the organization can make better-informed decisions
- Set up and manage AI development and product infrastructure
- Be a good team player, as coordinating with others is a must
Skills Required to Become an AI Engineer
Professionals seeking to become an AI engineer should also know about the skills required in this field. Some of them include:
1. Technical Skills
The first skill required to become an AI engineer is programming. To become well-versed in AI, it’s crucial to learn programming languages, such as Python, R, Java, and C++ to build and implement models.
Linear Algebra, Probability, and Statistics
To understand and implement different AI models—such as Hidden Markov models, Naive Bayes, Gaussian mixture models, and linear discriminant analysis—you must have detailed knowledge of linear algebra, probability, and statistics.
Spark and Big Data Technologies
AI engineers work with large volumes of data, which could be streaming or real-time production-level data in terabytes or petabytes. For such data, these engineers need to know about Spark and other big data technologies to make sense of it. Along with Apache Spark, one can also use other big data technologies, such as Hadoop, Cassandra, and MongoDB.
Algorithms and Frameworks
Understanding how machine learning algorithms like linear regression, KNN, Naive Bayes, Support Vector Machine, and others work will help you implement machine learning models with ease. Additionally, to build AI models with unstructured data, you should understand deep learning algorithms (like a convolutional neural network, recurrent neural network, and generative adversarial network) and implement them using a framework. Some of the frameworks used in artificial intelligence are PyTorch, Theano, TensorFlow, and Caffe.
2. People Skills
The difference between successful engineers and those who struggle is rooted in their soft skills. Although being an AI engineer is primarily a technical job, the ability to effectively communicate with others, deal with problems, organize one’s time effectively, and work in collaboration with others are the key factors that determine whether or not a project is successfully completed and delivered.
There is a broad range of people with different levels of competence that artificial intelligence engineers have to talk to. Suppose that your company asks you to create and deliver a new artificial intelligence model to every division inside the company. If you want to convey complicated thoughts and concepts to a wide audience, you’ll probably want to brush up on your written and spoken communication abilities.
To be a successful data scientist or software engineer, you must be able to think creatively and solve problems. Because artificial intelligence seeks to address problems as they emerge in real-time, it necessitates the development of problem-solving skills that are both critical and creative.
Engineers in the field of machine learning must recognize both the demands of the company and the sorts of obstacles their designs are addressing in order to create self-running programs and optimize solutions utilized by organizations and customers. A lack of expertise in the relevant field might lead to suggestions that are inaccurate, work that is incomplete, and a model that is difficult to assess.
Engineers in the field of artificial intelligence must balance the needs of several stakeholders with the need to do research, organize and plan projects, create software, and thoroughly test it. The ability to effectively manage one’s time is essential to becoming a productive member of the team.
Members of the AI and IT communities often collaborate with one another. The ability to operate successfully and productively in a team is a valuable skill to have. You may be required to work with both small and big groups to accomplish complicated objectives. Taking into account the opinions of others and offering your own via clear and concise communication may help you become a successful member of a team.
The majority of problems relating to the management of an organization may be resolved by means of successful artificial intelligence initiatives. If you have business intelligence, you will be able to transform your technological ideas into productive commercial ventures. You may strive to establish a fundamental grasp of how companies function, the audiences they cater to, and the rivalry within the market, regardless of the sector in which you are currently employed.
Critical Thinking Ability
Creative AI models and technology solutions may need to come up with a multitude of answers to a single issue. You would also have to swiftly evaluate the given facts to form reasonable conclusions. You can acquire and strengthen most of these capabilities while earning your bachelor’s degree, but you may explore for extra experiences and chances to expand your talents in this area if you want to.
AI Engineer Salary
According to Glassdoor, the average annual salary of an AI engineer is $114,121 in the United States and ₹765,353 in India. The salary may differ in several organizations, and with the knowledge and expertise you bring to the table.
Find Our Artificial Intelligence Course in Top Cities
How to Build a Career in AI
In addition to learning how to become an AI engineer, it is important to also understand which industry you want to work with. Since several industries around the world use AI to some degree or other, including healthcare and education, there has been exponential growth in the career opportunities within the field of AI. Some of these job roles are:
An AI developer works closely with electrical engineers and develops software to create artificially intelligent robots.
AI architects work closely with clients to provide constructive business and system integration services. They also create and maintain the entire architecture.
Machine Learning Engineer
Machine learning, or ML engineers build predictive models using vast volumes of data. They have in-depth knowledge of machine learning algorithms, deep learning algorithms, and deep learning frameworks.
Data scientists collect, clean, analyze, and interpret large and complex datasets by leveraging both machine learning and predictive analytics.
Business Intelligence Developer
They’re responsible for designing, modeling, and analyzing complex data to identify business and market trends.
Also Read: Deepak Gupta wanted to switch careers to a more in-demand industry and decided to upskill with our Artificial Engineer Master’s Program. Read his Simplilearn Artificial Intelligence Engineer Review; he started a new career at Tech Mahindra and even secured an 83% hike!
Here’s How Simplilearn Can Help You
Simplilearn offers various Artificial Intelligence courses, especially for professionals willing to know how to become an AI engineer. The master’s program consists of a number of practical courses, such as:
- Introduction to Artificial Intelligence
- Data Science with Python
- Machine Learning
- Deep Learning Fundamentals by IBM
- Deep Learning with TensorFlow
- AI Capstone Project and much more
Now that you have understood how to become an AI engineer, get started on your journey to becoming a certified AI Engineer and prepare yourself for AI and Data Science interview questions and landing at your dream job!
1) Is AI engineering a good career?
Yes, AI engineering is a rapidly growing and in-demand career field with a promising future. As organizations continue to adopt AI technologies, the demand for skilled AI engineers is only expected to increase. AI engineers can work in various industries and domains, such as healthcare, finance, manufacturing, and more, with opportunities for career growth and development.
2) Is AI engineering hard?
AI engineering can be challenging, especially for those who are new to the field and have limited experience in computer science, programming, and mathematics. However, with the right training, practice, and dedication, anyone can learn and become proficient in AI engineering. It requires a strong foundation in computer science, knowledge of machine learning algorithms, proficiency in programming languages like Python, and experience in data management and analysis.
3) Can You Become an AI Engineer Without a Degree?
While having a degree in a related field can be helpful, it is possible to become an AI engineer without a degree. Many successful AI engineers have backgrounds in computer science, mathematics, or statistics, but there are also a growing number of online courses, bootcamps, and other training programs that offer practical experience in AI development. It is important to have a solid foundation in programming, data structures, and algorithms, and to be willing to continually learn and stay up-to-date with the latest developments in the field.
4) How long does it take to become an AI engineer?
The time it takes to become an AI engineer depends on several factors such as your current level of knowledge, experience, and the learning path you choose. However, on average, it may take around 6 to 12 months to gain the necessary skills and knowledge to become an AI engineer. This can vary depending on the intensity of the learning program and the amount of time you devote to it.