The article is written in the masculine gender but addresses everyone.
It’s no secret that artificial intelligence or AI is an evolving technological trend. According to a Gartner report, AI is poised to create 2.3 million jobs by 2020, instead of the 1.8 million it will eliminate. Job growth has already flooded the industry, as the demand for someone with AI skills has already doubled over the past few years. So, in this article, we will guide you down the path of how to become an AI engineer in the following order:
What is artificial intelligence?
AI is a technique that allows machines to mimic human behavior. Artificial intelligence is the theory and development of computer systems capable of performing tasks that normally require human intelligence, such as visual perception, speech recognition, decision making, and translation between languages.
If you ask us, artificial intelligence is a simulation of human intelligence made by machines programmed by us. The machines have to learn to rationalize and self-correct as needed along the way.
Who is an AI engineer?
An AI engineer works with algorithms, neural networks, and other tools to advance the field of artificial intelligence in some way.
These professionals may work on different types of artificial intelligence in different industries, for example: healthcare, retail.
Artificial Intelligence Engineer
An AI engineer is someone who works on AI problems or technologies.
Engineers can also choose between projects that involve weak or strong AI, with different settings focusing on different capabilities. That’s a topic for another discussion.
Now there are many ways you can get there. So let’s see how to become an artificial intelligence engineer.
How to become an AI engineer
1. Now, if we start from the basics, you first need to earn a bachelor’s degree. This can be from one of the following areas or from the following topics:
- computer science
- Mathematics
- Information Technology
- statistics
- Afford
- economics
2. The next step is to fine-tune your technical skills.
One important thing to note here is that to be an AI engineer you need not only to be good at programming but also good at software development techniques and methods.
They should have theoretical and practical knowledge in the following subjects:
- Software development lifecycle
- Modularity, OOPS, Lessons
- Design templates
- Statistics and mathematics
- Machine learning
- Deep learning and neural networks
- Electronics, Robotics and Instrumentation (not mandated)
3. Apart from technical skills, there are also business skills that must be present when planning how to become a successful AI engineer. Some of these skills include:
- Analytical problem solving
- Effective communication
- Creative thinking
- Industry knowledge
4. Now, these skills can be achieved through practice or by choosing a master’s degree .
Since AI is a new topic in today’s world, a lot of recent discoveries and studies are happening that can be useful for your thesis.
It is recommended to go to a master’s degree in data science, machine learning or computer science.
Another option is to go for industry certifications for machine learning, deep learning or data science.
This will add great value to your resume and help you gain in-depth knowledge of topics both theoretically and practically.
What, in turn, will help you gain an advantage over other competitors.
Required skills
In the roadmap of how to become an AI engineer, we saw how many technical and business skills are required.
Let’s take a closer look at these skills. Starts with technical skills:
Technical skills
Programming languages (R/Java/Python/C++)
You have to be good at programming languages and not only is it important to have a solid understanding of departments and data structures.
Programming Languages – Artificial Intelligence Engineer
Sometimes Python will not suffice. You often come across projects that need to leverage hardware for speed improvements.
Make sure you’re familiar with basic algorithms, as well as lessons, memory management, and linking.
Linear Algebra/Calculus/Statistics
You will need to be closely familiar with matrices, vectors and matrix multiplication. If you have an understanding of derivatives and integrals, you need to be clear. Statistics are going to go up a lot.
At least make sure you are familiar with Gaussian distributions, means, and standard deviations. You need to have a solid understanding of probability to understand models like
- Naivety
- Gausian blend models
- Hidden Markov models
Applied mathematics and algorithms
A solid understanding of algorithm theory and knowledge of how the algorithm works is very important.
You’ll need to understand topics like incline decrease, convex optimization, Lagrange, quadratic programming, partial differential equation, and summaries.
All this math might seem intimidating at first if you’ve been away from it for a while.
Yes, machine learning and artificial intelligence is much more intensive in math than something like front-end development.
Language, audio and video processing
Natural language processing combines two of the main areas of work, namely.
Linguistics and computer science and chances are that at some point you will work with text or audio or video.
NLP
So there is a need for good control of libraries like Gensim, NLTK and techniques like word2vec, sentimental analysis and summary.
Neural network architectures
Neural networks
We need machine learning for tasks that are too complex for humans to code directly, that is, tasks that are so complex that it is impractical.
Neural networks have by far been the most accurate way to approach many problems, such as translation, speech recognition and image classification, which plays a very important role in the artificial intelligence department.
Aside from these technical skills, there are certain non-technical or business skills that are also required to become one successful AI engineer.
So let’s continue this article on how to become an artificial intelligence engineer to understand what exactly I mean by non-technical skills.
Business/non-technical skills
communication
You’ll need to explain ML and AI concepts to people with little expertise in the field. You may also need to learn from electrical and robotics professionals.
Communication is going to make it all that a lot easier.
Creative and critical thinking
AI engineers must look at the numbers, trends, and data and come to new conclusions based on the findings.
Exploring established business practices and brainstorming new approaches to artificial intelligence.
Whether you look at the rise of artificial intelligence will arouse excitement or apprehension, robots are here to stay in one form or another.
Combining this new technology with the most powerful features of the unique human brain offers a world of potential that has not yet been fully explored.
Rapid prototyping
Prototype
Integration on ideas as quickly as possible is a must to find one that works. In machine learning, this applies to everything from choosing the right model to working on projects like split testing. You need to make a set of techniques that are used for the rapid production of a miniature model of a physical part or assembly using three-dimensional design with the help of a computer, especially while working with three-dimensional models.
Industry knowledge
The most successful AI projects out there will be those that address real pain points. It doesn’t matter what industry you work in.
It’s helpful to know how this industry works and what will benefit your business.
If an AI engineer does not have business sense and knowledge of the components that make up a successful business model, all these technical skills cannot be productively channeled.
Now, with these skills, it is quite possible to get a job as an artificial intelligence engineer, but once you work you should also know what exactly you will be doing in everyday life. So here are the main roles and responsibilities of an artificial intelligence engineer.
Roles and responsibilities
Depending on their level of expertise, AI engineers in general:
- Learn and modify data science prototypes
- Research and implementation of ML algorithms and appropriate AI tools
- Machine learning application development according to requirements
- Working with electrical engineers and robotics team
- Select appropriate data sets and representation methods
- Run machine learning/AI testing and experimentation
- Training and training systems when needed
- Keep up with industry developments
Companies that employ AI-leading talent range from startups like Argo AI to tech giants like IBM.
According to Glassdoor, these are the top employers that have hired top AI talent in the past year.
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