To become an Artificial Intelligence Engineer, you need to fulfill the minimum criteria, which I will explain in this blog. However, before that, you must know whether or not AI as a career is suitable for you. Evidently, the global AI market is experiencing a mammoth growth of 154 percent. It is nothing short of a revolution! So, what has fueled this AI revolution? There are three major factors that contribute to its exponential growth:
- Maturing of Machine Learning
- The wide-spread use of Cloud Computing resources
- Betterment of data gathering, storing, and processing methods
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As the domain of AI is new, there are still many sectors that have not yet used AI. So, before the ‘how’ part, let us briefly squint to the ‘why’ part. Let us see the trends that Gartner predicts about Artificial Intelligence:
- AI will create a business value worth US$3.9 trillion by 2022.
- Artificial Intelligence is expected to be the most disruptive technology category in the next decade due to the advances in computing power, capacity, speed, and data diversity, and progresses in deep neural networks (DNN)
- Decision automation systems (systems that leverage AI to automate business processes or tasks such as translating voice, classify data which cannot be easily classified by conventional systems, etc.) will grow to 16 percent by the next 4–5 years—a staggering jump of 14 percent!
Who is an Artificial Intelligence Engineer?
Artificial Intelligence Engineers (AI Engineers) are people involved in problem-solving or Artificial Intelligence technology. They create, test, and implement AI models, along with the handling of the AI infrastructure. They cruise through traditional software models and Machine Learning models. With the development of Artificial Intelligence and Machine Learning in different areas of the IT industry, the demand for these IT professionals is high.
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How to become an AI Engineer?
There are a few prerequisites that you need to meet and a few AI Engineer skills that you have to learn to become an AI Engineer. Here are those:
- Bachelor’s degree in IT, Computer Science, Statistics, Data Science, Finance, etc.
- Master’s degree in Computer Science, Mathematics, Cognitive Science, Data Science, etc.
- Certifications in Data Science, Machine Learning, etc.
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Technical/Non-technical AI Engineer Skills to Master
To become an AI Engineer, you must have a few technical and non-technical AI Engineer skills. Let us have a close look at these skills:
To become a successful AI Engineer, you have to become proficient in a few programming languages. You need to pick one or more languages that will help you explore and implement the capabilities of AI. A few of the many languages that work well with AI are:
- Prolog, etc.
Each language satisfies the different needs of AI. Although you cannot learn all, you can start with a language that fits your learning curve and helps you get started with AI. Now, you must be wondering, which one is the best? Evidently, Python is a favorite of developers and AI Engineers, just because of its simplicity, clear syntax, versatility, and a large library.
You can also start with Java or C++, but you will face portability and multi-tasking issues. Also, Python would not be enough, so we recommend to start with Python or R and eventually move to learn algorithms.
Statistics is the backbone of any algorithm. As a matter of fact, your entire AI program will depend on the type of algorithm that you use. Now, to work on an algorithm, you will need considerable knowledge of statistics, calculus, or algebra, and probability to boost your AI program. Moreover, in your AI journey, you will encounter Machine Learning models like Naïve Bayes, Hidden Markov, etc., which require a firm understanding of probability.
Algorithms and Applied Mathematics
To build a model or use an existing one, you must have a good knowledge of algorithms. Also, to grasp the concepts of an algorithm, you must have a solid understanding of applied mathematics. Areas, where you will be frequently using your algorithms and applied mathematics knowledge, are:
- Quadratic programming
- Partial differential equations
- Gradient descent
- Convex Optimization, etc.
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Natural Language Processing
Natural Language Processing (NLP) is all about combining computer science, information engineering, linguistics, and AI into one and programming the system to process and analyze large datasets. Therefore, as an AI Engineer, you have to work extensively on NLP, which involves language, audio, and video processing by leveraging various NLP libraries and tools, including:
- Sentiment Analytics
- PyNLPI, etc.
A neural network is simply a system (software or hardware) that works similar to a human brain. As per the neural functionality of a human brain, the concept of artificial neural networks is developed. It has many commercial and business applications, wherein as an AI Engineer, you will solve complex problems in the areas of pattern recognition, facial recognition, handwriting recognition, etc.
Moving ahead, let us focus on a few Non-technical skills of an AI Engineer.
Communication and Collaboration Skills of an AI Engineer
As an AI Engineer, you will be working extensively on data. Thus, your stakeholders will be depending on you to solve their critical problems. To do that, you need to communicate your findings in an efficient manner. Also, having good communication skills will make you go to places and establish a good rapport with your stakeholders.
To become an AI Engineer, you have to gear up fast to fact-check the numbers and data. This requires analytical thinking. You also have to put questions to the data analytics team to ascertain the feasibility of data and brainstorm with the key stakeholders.
The most successful, also common, Artificial Intelligence project requires working on the front and solving the vulnerable pain points. Therefore, no matter which industry you are in, you must be aware of the market, customers, and how the core business runs. If you do not possess the business acumen to translate your technical ideas into a successful business model, then having those technical capabilities will be of less use.
Iteration of Ideas (Rapid Prototyping)
In order to find a viable idea, you must consider a number of ideas as soon as possible. This applies to the selection of the right model, A/B testing, NLP libraries, etc. You must use a variety of techniques to quickly create realistic scale models of solid parts or assemblies using 3D computer-aided designs, especially when using 3D models.
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AI Engineer Roles and Responsibilities
AI Engineer Salary
AI Engineer Salary in the US: The average annual salary of entry-level AI Engineers is US$71,600, while the average annual salary of experienced AI professionals is US$248,625.
AI Engineer Salary in India: AI Engineers in India have an average annual salary of ₹1,400,000. The starting salary is approximately ₹600,000 per year. The annual salary of mid-level and senior AI professionals may exceed ₹5,000,000/year.
This brings us to the end of this article. I hope that now you have an understanding of the profile of an Artificial Intelligence Engineer and know what path you need to take to make a career in AI. Now that you are aware of the key technical and non-technical AI Engineer skills that you need to master to become an Artificial Intelligence Engineer, you must enroll in Intellipaat’s AI Master’s Course to make a smooth transition into AI.
With our well-designed Artificial Intelligence course curriculum, you can easily master technologies such as supervised learning, unsupervised learning, natural language processing, and much more.
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