You need to fulfill some minimum criteria to become an AI engineer, but before that, you must know whether or not AI is a suitable career for you. Evidently, the global AI market is experiencing a meteoric growth of 154 percent. It is nothing short of a revolution! So, what has fueled this AI revolution? There are three major factors that have contributed to AI’s exponential growth:
- Maturing of machine learning
- Widespread use of cloud computing resources
- Betterment of data gathering, storing, and processing methods
The following topics will be covered in this blog:
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Let us take a look at the trends that Gartner predicts about AI:
- AI will create a business value worth US$3.9 trillion by 2022.
- AI is expected to be the most disruptive technology category in the next decade due to the advances in computing power, capacity, speed, data diversity, and progress in deep neural networks (DNN).
- Decision automation systems, which are systems that leverage AI to automate business processes or tasks such as translating voice, classifying data that cannot be easily classified by conventional systems, etc., will grow to 16 percent in the next four to five years—a staggering jump of 14 percent!
Who is an AI Engineer?
AI engineers create, test, and implement AI models as well as handle the AI infrastructure. They cruise through traditional software and machine learning models, and with the development of AI and machine learning in different areas of the IT industry, the demand for these AI professionals is on the rise.
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How to Become an AI Engineer?
There are a few AI engineer skills you need to have and a few educational prerequisites that you need to fulfill in order to become an AI engineer. The following are those skills:
Educational Prerequisites
- 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 or Nontechnical AI Engineer Skills to Master
To become an AI engineer, you must have a few technical and nontechnical AI engineer skills. Let us take a closer look at them:
Programming Languages
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:
Each language satisfies the different needs of AI. Although you cannot learn all of them, 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 large library.
You can also start with Java or C++, but you will face portability and multitasking issues. Python will not be enough, so we recommend starting with Python or R, and, eventually, moving on to learn other languages and algorithms.
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Statistics/Calculus/Algebra
Statistics is the backbone of any algorithm. As a matter of fact, your entire AI program depends on the type of algorithm that is used by you. Now, to work on an algorithm, you will need considerable knowledge of statistics, calculus, and/or algebra to boost your AI program. Moreover, in your AI journey, you will encounter machine learning models, such as Naïve Bayes, Hidden Markov, etc., that will 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, and to grasp the concepts of an algorithm, you must have a solid understanding of applied mathematics. The areas where you will be frequently using your knowledge of algorithms and applied mathematics are:
- Quadratic programming
- Partial differential equations
- Gradient descent
- Lagrange
- Convex optimization
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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 will have to work extensively on NLP, which involves language, audio, and video processing by leveraging various NLP libraries and tools including:
- NTLK
- Sentiment Analytics
- Gensim
- TextBlob
- CoreNLP
- PyNLPI
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Neural Networks
A neural network is simply a system, software, or hardware, that works similarly to a human brain. The concept of artificial neural networks is developed as per the neural functionality of the human brain. Neural networks have many commercial and business applications wherein as an AI engineer, you will have to solve complex problems in the areas of pattern recognition, facial recognition, handwriting recognition, etc.
Moving ahead, let us focus on a few nontechnical skills of an AI engineer.
Communication and Collaboration Skills
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. Having good communication skills will also make you go to places and establish a good rapport with the stakeholders.
Analytical Skills
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.
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Business Acumen
AI projects require working on the front end and solving vulnerable pain points. Irrespective of the industry you are in, it is absolutely important that you are 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 is of no use.
Iteration of Ideas—Rapid Prototyping
Idea iteration is sometimes also referred to as rapid prototyping. Basically, what happens in rapid prototyping is that 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 is a blooming field and has lucrative job opportunities for skilled professionals. The average salary for an entry-level AI professional in India has been reported, by Glassdoor, to be somewhere between INR600,000 p.a. to INR800,000 p.a., while a mid- to senior-level AI professional earns somewhere between INR900,000 p.a. to INR1,200,000 p.a. However, with experience this figure can reach INR2,000,000 p.a. Some companies, such as RazorThink, Accenture, ZestMoney, etc., are known to offer a higher range of salary to AI engineers.
In the US, the average salary of an AI engineer is US$183,734 p.a. On average, experienced AI engineers make somewhere between US$1,300,000 p.a. to US$1,400,000.
Conclusion
We hope that this blog will help you in your decision to chart out the right AI career path for you. Now that you are aware of the key technical and nontechnical skills needed to become an AI engineer, you must reap the benefits of Intellipaat’s AI Master’s Course to make a smooth transition into AI.
With our expertly-designed AI course curriculum, you can easily master technologies such as supervised learning, unsupervised learning, NLP, etc.
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