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.

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.

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.

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.

You have the option of enrolling 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 field that is conceptually comparable, such as information technology or computer science, and then specialize in artificial intelligence alongside data science and machine learning.