Blogskeyboard_arrow_rightHow to Become an AI Engineer

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Artificial Intelligence

How to Become an AI Engineer

Published by SuperDataScience Team

Monday Jun 24, 2019

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The ultimate learning path guide detailing all the skills, knowledge and training you need to become a professional AI engineer

If you enjoy mathematics, data and computers, then working as an AI Engineer is an exciting career option.
AI is an emerging technology that’s still in its relative infancy, but will become more and more commonplace in the near future.

Stanford University’s 2018 AI Index report revealed that there has been a 1400% increase in AI startups since the year 2000 and that there has been a 4.5x increase in AI-related jobs since 2013.
In other words, it’s a sector of business that is growing rapidly.

This guide is designed to lay out everything you need to know on your journey to becoming a successful AI engineer.

AI Leading Lights
Let’s start by looking at some of the most well-known and influential AI engineers, to give us an idea of what the job involves, the types of projects you can get involved in and what you can achieve.

Andrew Ng: One of the most famous AI engineers of recent years. Andrew set up and led the Google Brain project and also headed up the AI group at Baidu. His main focus has been on machine learning, neural networks and deep learning.
Daphne Koller: Professor of Computer Science at Stanford and co-founded Coursera with Andrew Ng. Coursera has become one of the world’s most popular free course providers. Her expert field is Bayesian machine learning and applying it to biomedicine.
Geoffrey Hinton: Well-known computer scientist, AI engineer and psychologist who broke new ground in artificial neural networks.
Jurgen Schmidhuber: A renowned computer scientist and AI expert who has been pivotal in the research and development of the AI behind self-driving cars.

Hopefully seeing this short list (there are dozens more names we could add) inspires and motivates you to follow a career as an AI engineer.


Salary and Employability
The big picture is very good for AI engineers, both in terms of job opportunities and potential earnings.

According to Indeed.com, the average salary for an AI or Machine Learning Engineer is $140,866, placing it as one of the highest paid technical professions you can get at the moment.
Employer demand for AI engineers has skyrocketed over the past 3 years, with 2018’s growth rate at a massive 344%!

If you have the ability and you’re willing to learn the technical know-how, now is the time to become an AI engineer. There is fast-growing demand and a shortage of skills, making it one of the safest bets for a long and prosperous career.


What do AI engineers do?
AI or machine learning engineers work on a variety of projects. Depending on the role, the main responsibilities of the job could be:

  • Computer programming, using languages such as Java, Python or C#
  • Working with data, statistics and algorithms
  • Using data modelling and evaluation strategies
  • Applying machine learning algorithms and libraries
  • Research and design deep learning applications

There are many other things that AI engineers may work on, many will be specific to the project. For instance, if you’re working on self-driving cars there could be elements of computer vision, creating deep learning models, sensor fusion and filtering and programming.

In general, the role of an AI engineer can be broken down into 4 categories:

  1. AI or Machine Learning Researcher - This role involves exploring the theoretical side of AI, and looking to further the development of the technology or apply it to new areas. They are often Masters or PhD educated.
  2. AI Software Developer or Program Manager - These engineers apply machine learning to a given data set. Strong programming and mathematical skills are needed for this role.
  3. Data Analysis and Data Mining Engineers - Modelling and creating deep-learning systems to recognize and respond to patterns.
  4. Machine Learning Engineers - This covers everything else AI-related - i.e. industry-specific machine learning applications. In other words, using and applying AI techniques to perform functions or solve problems in a business setting.

Steps to Becoming an AI Engineer


1. Gain Qualifications
Gaining the right qualifications is an important part of the journey to becoming an AI engineer. The first decision is whether to study for a college degree or not, and if so, which course to take.

The best way to decide whether to study for an undergraduate degree in computer science or an AI-related subject, is to ask yourself whether you want to be involved in research and development or not. If the answer is yes, then a mathematics, computer science or dedicated Artificial Intelligence degree, followed by a masters or PhD in machine learning is recommended.

If you’re not interested in the research side of things and want to be more hands-on with the application of AI, then you don’t necessarily need a degree. College degrees have their limitations, especially in an emerging field like AI, as the stuff you’ll learn is already out of date by the time the curriculum is taught.

Online courses are often the best option, as they are up-to-date, often teaching cutting-edge techniques, and they are becoming increasingly respected by employers, both big and small.

2. Develop Skills and Knowledge
You will need to develop both technical and personal skills to be a successful AI engineer. Some of the technical skills you should develop are:

  • Programming - at least one of the following: Python, Java, C#
  • Artificial Intelligence theory and techniques
  • NLP and deep-learning
  • Data Science applications
  • Computer vision

The soft skills you should develop are:
  • Problem solving and analytical thinking
  • Patience and resilience
  • Working independently or as part of a team
  • Attention to detail
  • Focus
  • Willingness to learn new skills and acquire new knowledge

3. Build a Portfolio of Work Experience
Because AI is an emerging technology, employers aren’t necessarily interested in formal degree qualifications. What they are looking for is a blend of online training, reading around the subject and hands-on experience.

The more experience you can demonstrate, the more likely you are to land a well-paid AI engineer job.

We recommend that as soon as you start your journey to becoming an AI engineer, that you begin compiling a portfolio of relevant experience. This can include:

  • Courses - Online or real-life courses will often get you working on AI projects such as creating deep learning models or programming applications. Keep a record of them and any certificates you receive.
  • Workshops - Any AI-related workshops you attend, live or online, are ideal for demonstrating specific knowledge. If you complete a workshop on computer vision for example, this will make you desirable for many roles involving robotics or automated tasks.
  • Paid or unpaid work - Include any work you do, whether paid or not. Even if it’s for a friend’s website - programming their chatbot, for example. This gives you demonstrable skills that you can link directly to.
  • Extensive reading or extra study - Employers are looking for people who are passionate about AI. Record any books, magazines or respected online publications you read, or better yet, write a short summary of what you learned.
  • Related hobbies or interests - If you’ve always enjoyed coding as a hobby, or other technical things, include them. This can also be a good way to showcase your soft skills. Team sports to demonstrate collaborative working, or crossword solving to show analytical thinking, for instance.

If you decide to enroll in any SuperDataScience courses, you automatically get a professional portfolio created for you. It will update to include any specialist AI-related workshops you’re involved in. Click here to discover more about our courses.

Specialist AI Engineer E-learning Courses
Ok, so if you like the sound of working as an AI engineer, you’re ready to move onto the next stage. Getting the knowledge and skills you need.

At SuperDataScience, we have put together a wide-range of courses that will help you to become a professional AI engineer. Our aim is to make complex subjects simple to learn.


SuperDataScience Ultimate Learning Path
Our AI engineer learning path is a series of courses that build up the knowledge and skills you need to become an AI engineer. The content is 100% up-to-date and is at the cutting-edge of AI application.

At the end of each course you’ll receive a completion certificate and any AI workshops you participate in will be automatically added to your professional portfolio.

We recommend that you take the following courses in this order:

A course that dives deep into deep learning. Everything you need to know, explained simply and clearly to avoid confusion.

As well as learning hands-on coding, intuition and how to use machine learning tools, you’ll also get the chance to work on 6 exciting and real-life projects to apply your new skills to:
  • Artificial Neural Networks to solve a Customer Churn problem
  • Convolutional Neural Networks for Image Recognition
  • Recurrent Neural Networks to predict Stock Prices
  • Self-Organizing Maps to investigate Fraud
  • Boltzmann Machines to create a Recommender System
  • Stacked Autoencoders to take on the challenge for the Netflix $1 Million prize


A comprehensive course that takes you from complete AI beginner to expert.
You’ll master all the essential skills, develop your intuition, and practice your skills on a variety of hands-on applications.
  • Develop Q-learning intuition
  • Build deep learning and AI for a self-driving car
  • Learn and apply Deep Convolutional Q-learning techniques
  • Understand and design artificial neural networks
  • Understand and design convolutional neural networks

This course will give you skills that ALL businesses want and need. You’ll learn how to apply AI to:
1. 1. Optimize Business Processes
2. 2. Minimize Costs
3. 3. Maximize Revenue

You’ll learn how to identify business optimization opportunities, develop AI solutions and apply them.

As well as video tutorials and real-life exercises to practice on, you’ll also get an in-depth 100 page e-book that covers every aspect of AI for business.

Computer vision is, without a doubt, the biggest sector of the AI industry at the moment, with plenty of job opportunities for AI engineers ($18 billion market!). It’s also one of the easiest skills to learn and apply.

This course makes it especially simple as it is highly structured with hands-on examples and exercises. You’ll discover:
  • How to develop facial recognition and object detection intuition
  • How to apply general adversarial networks to create images
  • Artificial and convolutional neural networks
  • Plus many more important AI skills.


The ultimate deep learning and natural language processing (NLP) course. This course is 100% geared towards a career as an AI engineer. You’ll discover:
  • Deep NLP intuition
  • How to build an NLP chatbot
  • Data preprocessing techniques
  • Building, training and testing SEQ2SEQ models
  • Artificial and recurrent neural networks


This is the most hands-on data science course available online. You’ll get to practice important AI engineer skills such as data mining and modelling.

You’ll gain a good understanding of SQL, SSIS, Tableau and Gretl, which are essential for a career in AI and machine learning.

By the end of the course you’ll be an expert at:
  • Cleaning and preparing data
  • Presenting basic data visualizations
  • Modelling data
  • Curve-fitting
  • And more!

Data visualization is an important skill for AI engineers. You need to be competent with preparing, analyzing and interpreting data too.

This course will teach you to use the ultimate data analysis and visualization tool - Tableau 2018. You’ll discover:
  • How to create a variety of graphs and charts
  • Advanced data preparation techniques
  • How to join and blend data
  • Plus much, much more!

This course will teach you how to master using Business Process Model and Notation Version 2.0 Standards and Practices. You’ll get the chance to apply what you’ve learned to real-life business examples, which is good preparation and experience for your career.

By the end of this course you’ll be able to:
  • Model any existing business process
  • Create models for new processes
  • Apply AI principles to business problems

Boost your employability with this course on how to get a job in data science. You’ll learn:
  • The exact requirements for data science jobs
  • How to write an outstanding application
  • How to excel at interview
  • Tips to becoming a top data scientist/AI engineer

Start your Exciting Journey to Becoming an AI Engineer
Don’t delay! Now’s the time to get the skills you need to get a career in one of the most in-demand jobs at the moment, and for the foreseeable future.

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