Before mastering any of the skills of an Artificial Intelligence (AI) Engineer, you must understand the ecosystem of AI. Today, AI is used in different areas such as robotics, image processing, computer vision, chatbots, natural language processing, gesture control, speech recognition, and more. Each of these domains requires specific AI platforms or components.
In all business areas and industries, stakeholders and managers are constantly debating over the benefits of AI. Usually, the possibilities with AI seem to be endless. Thus, ‘now’ is the best time to learn AI.
What skills do you need to be an AI Engineer?
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Technical Artificial Intelligence Engineer Skills
To ride on the promising AI bandwagon, first, you have to master a few AI Engineer skills. Here are some of the major technical skills required for AI Engineers.
Well-versed with Various Programming Languages
To master Artificial Intelligence and Machine Learning, you will eventually have to become proficient in certain programming languages such as R, Python, Java, C++, and more. While Python will make it easy for you to create complex algorithms, C++ can accelerate the encoding process.
For statistics, you have to learn R. Furthermore, you will be needing Java to implement mappers and reducers—it is the best for both. Now, to upskill your programming languages in a short amount of time, we recommend exploring various open-source libraries that these languages have to offer.
Solid Knowledge of Mathematical and Algorithms
The ideal AI Engineer works extensively on applied mathematics and algorithms. It is one of the most important skills required for AI Engineers. These professionals must be familiar with problem-solving and analysis skills, along with using their mathematical knowledge to devise efficient handling of AI tasks.
Most of the top companies that hire AI Engineers want a solid knowledge of statistics, probability, and applied math in the AI candidates, which they can utilize to solve their key business pain points. If you have a solid mathematical knowledge, then you can create or design algorithms suiting to the requirements of your stakeholders. Evidently, professionals with a master’s or Ph.D. degree in statistics are the most sought-after AI candidates by the IT moguls. They even pay US$250,000 for such candidates.
Distributed Computing Efficiency
Most AI jobs require programmers to deal with large amounts of data that cannot be processed on a single computer. Here, the data must be evenly distributed across a cluster. Therefore, AI aspirants must improve their knowledge of distributed computing.
As you tread further in your AI career, you will see that a majority of data processing in AI is done on Linux-based systems. If you are not accustomed to utilizing certain UNIX tools, including grep, awk, sort, find, cut, tr, and so on, then you would not be able to grasp the functions of AI in the OS environment.
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Knowledge of Advanced Signal Processing Techniques
One of the key traits of Machine Learning is its feature extraction process, which allows the transformation of pixel values into meaningful data or information. Now, to master this, you must be accustomed to solving problems using advanced signal processing algorithms. Also, to hold a substantive understanding of the time-frequency analysis, you must work on projects that could help you understand these in a better way. Eventually, you will need other important AI concepts such as Fourier analysis, convolution, and more.
Familiarity with Machine Learning Algorithms and Libraries
All the standard implementations of ML algorithms are distributed in libraries, APIs, or packages, including TensorFlow, Spark MLlib, Scikit-learn, etc. However, the effective application of these algorithms requires:
- The selection of appropriate model or multiple models, which may include k-nearest neighbors, decision trees, support vector machines, etc.
- Learning procedures comprising linear regression, bagging, etc. to put the data into the algorithm
- Understanding the effect of hyperparameters
Understanding of Artificial Neural Networks
Artificial neural networks are essentially a set of algorithms or a system that replicates the working of a human brain. The concept of artificial neural networks has been developed based on the neuronal function of the brain. In today’s evolving AI-driven world, the scope of neural networks is quite large.
As an AI Engineer, you have to learn to solve complicated issues pertaining to face and pattern recognition, handwriting recognition, etc.
Non-technical Artificial Engineer Skills
Possessing only technical AI skills is not enough. You have to showcase your findings to stakeholders and collaborate with both internal and virtual teams on an everyday basis, in a non-technical manner. Thus, you have to equip yourself with a few non-technical Artificial Engineer skills as well. Here are those.
Interpersonal and Communication Skills
It is basically people skills. Learning these will help you collaborate with other teams, build a rapport with them, and communicate your findings with the stakeholders.
Problem-solving Skills
If you do not have problem-solving skills, then you cannot survive or evolve in your AI career. First, you must develop a critical problem-solving approach. It will help you think analytically, which will further assist you in identifying and analyzing complex problems
Industry Knowledge
To get a job as an AI Engineer, you must know the industry in which you are applying as an AI candidate. Since AI has seeped into almost all industries, start by identifying the job roles and functions and then learn about the industry.
Down the lane, you have to work closely with your stakeholders and customers to solve the key business issues. Therefore, you have to be aware of the industry for which you work.
Management and Leadership Skills
One of the key AI Engineer skills that every AI professional should possess is management and leadership skills. Acquiring these will allow you to access, analyze, and utilize your internal team members in an efficient way, thus, enhancing the overall productivity of the project.
AI Engineers Salary
Now, let us take a quick look at the structure of an AI Engineer’s salary based on experience.
- Entry-level AI Engineer Salary: US$71,600 per year
- Mid-level AI Engineer Salary: US$112,095 per year
- Senior-level AI Engineer Salary: US$248,625 per year
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