The artificial intelligence engineering market is in high demand and the reason is obvious. Artificial intelligence seems to have an infinite capacity to simplify and improve common human tasks such as speech recognition, image processing, business process management, and even disease diagnosis. 

If you already have a background in software programming and are interested in technical issues in this field, it is not bad to consider the lucrative profession of artificial intelligence and learn more about how to become an artificial intelligence engineer.

What is artificial intelligence?

Many people still think of robots when they hear the word artificial intelligence, and think that artificial intelligence is the same insensitive robot that is designed to do things more easily and is set to replace humans in the future.

The cause of this attitude is mostly science fiction films, but the reality is very different from what is imagined. Artificial intelligence, as it is known today, is actually a technology that is somehow capable of thinking. Artificial Intelligence refers to the ability of a computer system to mimic human behavior.

This type of intelligence is used in machines and can be compared to the natural intelligence used by humans and animals. In artificial intelligence, machines learn from past data and actions, whether positively or negatively.

What does an artificial intelligence engineer do?

Using machine learning algorithms and deep-learning neural networks, an AI engineer creates AI models to draw business perspectives that can be used to make business decisions that affect the entire organization. These engineers create strong or weak artificial intelligence, depending on what goals they pursue. Artificial intelligence engineers have a thorough understanding of programming, software engineering, and data science.

They use a variety of tools and techniques to process data and develop and maintain artificial intelligence systems.

Responsibilities of an AI Engineer

As an AI engineer, you are required to perform specific tasks such as developing, testing, and deploying AI models through programming algorithms such as random forest, logistic regression, linear regression, and so on. These tasks include the following:

•  Convert machine learning models to application programming interfaces (APIs) so that other applications can use them.
• Build individual components of AI models and help other parts of the organization (such as product managers and stakeholders) better understand the results of these models.
• Building application infrastructure and data conversion.
• Automation of the infrastructures used by the data science group.
• Perform statistical analysis and optimize results so that the organization can make better decisions.
•  Set up and manage the infrastructure needed to run smart models.
•  Ability to interact and coordinate with others to move things forward.

Skills needed to become an artificial intelligence engineer

Professionals wishing to become artificial intelligence engineers should be familiar with the skills required in this field. Some of these skills are as follows.

Programming skills 

The first skill you need for an AI engineer is programming. An AI engineer must be well versed in programming languages ​​such as Java, R, Python, and CiplusPlus to build and implement models. Having a strong understanding of classes and data structures is important, but in most cases, mastering software concepts is not enough to get things done. You may encounter projects that require hardware knowledge to progress. You should be familiar with basic algorithms, classes, memory management, and pointer topics.

Linear algebra, probability, and statistics

To understand and implement various AI models, including the Markov latent model, the Gaussian mixed model, etc., you must have sufficient knowledge of linear algebra, probability, and statistics.

Big data and spark technologies

Artificial intelligence engineers work with large volumes of data, which can include streaming or real-time work with terabytes or petabytes of information. To work with such data, these engineers need to know about Spark and other big data technologies and how to use them. In addition to Apache Spark, other big data technologies such as Cassandra, Hadoop, and MongoDB can be used.

Algorithms and frameworks

Understanding how machine learning algorithms work, such as linear regression, KNN, Naive Bayes, Support Vector Machine, and more, can help you easily implement machine learning models. In addition, to build machine learning models with unstructured data, you need to know how to work with deep learning algorithms and implement them using an appropriate framework.

The most important frameworks used in the field of artificial intelligence, especially machine learning, are Tensorflow, PyTorch, Kafi, and Theano.

Natural Language Processing

Natural language processing combines the two main parts of machine learning and artificial intelligence, which include linguistics and computer science. You probably work with texts, sounds, and videos a lot during your career. Therefore, having skills in working with libraries such as Gensim, NLTK, and techniques such as word2vec, Sentimental Analysis, and Summarization are very important.

Neural networks and deep learning

Sometimes we may need machine learning to perform tasks that are very complex for humans to perform directly. This is where neural networks come into play. Neural networks are modeled on the human brain and can recognize numerical patterns based on sensory data. The world of artificial intelligence naturally transforms single-layer neural networks into deep learning neural networks in which data is passed through multiple layers to identify a more complex pattern.

Deep neural networks have so far been the most accurate way to deal with complex issues such as translation, speech recognition, and image classification and have played a very important role in advancing artificial intelligence.

Communication and problem-solving skills

Artificial intelligence engineers need to be able to communicate properly to introduce and present their ideas and products to stakeholders. In addition, they must have a great deal of problem-solving ability to overcome barriers to decision-making and paint a useful business vision. You need to explain the concepts of machine learning and artificial intelligence to people who do not have experience in this field.

In addition, you may need to get information from people who are in the field of electricity and robotics. Being equipped with communication skills makes all of this easier for an AI engineer.

Industrial knowledge

The most successful AI projects are those that work exactly on human needs. In any industry you work in, you need to know how that industry works and what benefits it. If an AI engineer does not have the industry acumen to discern the elements of a successful business, his or her technical skills cannot be used creatively with each other. With these skills, you can certainly find a job as an AI engineer, but when you work, you need to know exactly what to do daily.

Job opportunities and the amount of salary an AI engineer receives

According to Gartner, artificial intelligence will create nearly 2.5 million jobs by 2022. In addition, the demand for hiring an artificial intelligence engineer has doubled over the past few years.

The International Data Corporation (IDC) predicts that the number of artificial intelligence jobs in the world will grow by 16% in the coming years. The Covid epidemic has killed 19 million jobs, yet the Internet giants are expanding their use of artificial intelligence.

A 2017 report by the McKenzie Institute shows that one-third of workers in the United States will be replaced by automation and robots by 2030. Currently, the demand for hiring artificial intelligence professionals from small knowledge-based companies such as Argo AI to super-companies such as IBM, Microsoft, Amazon, Google, etc. is booming.

A qualified artificial intelligence engineer can easily find the job he or she wants around the world.

Certificate of training courses

You can acquire the skills mentioned by practicing or choosing a certification training course. To become an AI engineer, it is recommended to get a certificate in the fields of data science, machine learning, or artificial intelligence. These certifications increase the value of your resume and help you get closer to the maximum salary set for an AI engineer, in addition to mastering AI topics more fully.

According to Gartner, artificial intelligence jobs will be among the top IT jobs in the coming years. These cases show that a fresh and bright future lies ahead for people who are seriously investing in AI issues.