How To Become An AI Engineer? – Let’s Find Out!
Earn an educational qualification
Perfect your technical skills
Necessary Business Skills
To succeed in Artificial Intelligence jobs, you need to have an analytical thought process just like a data scientist and the ability to solve problems with cost-effective, efficient solutions. AI engineers also need to have insight into technological innovations that translate to state-of-the-art programs. The following topics provide an insight into the prerequisites that you must acquire to get the AI job of your dreams.
A) Earn an educational qualification
Let’s start with the basics. You need to earn a bachelor’s degree first. You can earn a degree in either of the following subjects to be an AI engineer: Computer Science, Mathematics, Information Technology, Statistics, Finance, and Economics.
B) Perfect your technical skills
If you are researching on how to become an AI engineer, you need to up your software development techniques and practices, along with your programming skills. Make sure that you are theoretically and practically well- informed in the following topics:
- Programming languages
- Statistical knowledge
- Applied Maths and Algorithms
- Natural Language Processing
- Deep Learning & neural networks
Let’s take a more in-depth look at the technical skills that are required.
B.I) Programming Languages
You need to have a very good grasp of programming languages, such as Python, R, Java, C++, and so on. It’s critical to have a robust understanding of classes and data structures. At times, just the knowledge of these technologies may not suffice. You might come across projects where you need to leverage hardware knowledge for enhancements. You must be familiar with basic algorithms, classes, memory management, and linking.
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B.II) Statistical Knowledge and Applied Math and Algorithms in Machine Learning
Coming to statistical knowledge, you will need to be intimately familiar with matrices, vectors, and matrix multiplication. If you have an understanding of derivatives and integrals, you should be in the clear. Statistics is going to come up a lot. At least make sure you are familiar with Gaussian distributions, means, and standard deviations. You must also have a firm understanding of Probability to understand models like:
- Naive Bayes
- Gaussian Mixture Models
- Hidden Markov Models
To be a successful AI engineer, You must have in-depth knowledge of algorithm theory and how algorithms work. Artificial Intelligence will need you to know subjects such as Gradient Descent, Lagrange, Quadratic Programming, Partial Differential equation, and so on. All this math might seem intimidating at first if you’ve been away from it for a while. Be prepared, Machine Learning and Artificial Intelligence are much more math-intensive than something like front-end development.
Read this blog about Machine Learning Algorithms to get a fair idea about algorithms required in ML.
B.III) Natural Language Processing
Natural Language Processing combines two of the major areas of Machine Learning and Artificial Intelligence: Linguistics and Computer Science. The possibility of you working with either text or audio or video is very high. Therefore, it’s important to have good control over libraries like Gensim, NLTK, and techniques like word2vec, Sentimental Analysis, and Summarization.
B.IV) Deep Learning & Neural Networks
At times, we might need Machine Learning for tasks that are too complex for humans to code directly. This is where neural networks come in. Neural networks are modeled after the human brain, which can recognize numerical patterns based on sensory data.
The Artificial Intelligence world has naturally progressed single-layer neural networks to Deep Learning neural networks, in which data is passed through multiple layers for more complex pattern recognition. Deep neural networks have been by far the most accurate way of approaching complex problems, like Translation, Speech Recognition, and Image Classification, which play a very important role in AI.
The following blog provides more in-depth knowledge about Deep Learning.
C) Necessary Business Skills
In order to secure an artificial intelligence job, you need to possess certain business skills along with your technical skills. The following are some of the business skills required to be a successful AI engineer:
- Creative thinking
- Effective communication
- Analytic problem-solving skills
- Industry knowledge
Certification Courses
You can accomplish the above-mentioned skills through practice or by choosing a certification course. To be an AI engineer, completing a certification course in Data Science, Machine Learning or Artificial Intelligence is highly recommended. These certifications will add value to your resume and will help you to acquire in-depth knowledge of AI topics, along with hiking up your pay to match an AI Engineer’s salary.
According to Gartner, Artificial Intelligence jobs will be among the top IT jobs in the near future. Also, this article by Springboard on the top AI jobs makes a nice read. Now that you have a fair idea of how to become an AI engineer, find out how Springboard can help you achieve your career goal. Springboard offers a 1:1 mentoring and project-led online learning program on Artificial Intelligence/Machine Learning with a job guarantee that equips you with job-ready skills that help you become hirable.
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