Last Updated on August 17, 2020 by Henry John



Are you aspiring to pursue a career in the promising field of artificial intelligence?



Do you feel willing but not fully prepared to pursue a career in the AI Industry?



And lastly, are you looking to fully prepare yourself for a successful career in AI but don’t know how or where to start?



Then worry not, if your answers are yes, yes, and yes because I got you.



Over the course of this post am going to walk you through the things you need to know and do to prepare for a success career in the Artificial Intelligence Industry.



But first, you need to know that Artificial Intelligence is complex and still developing both as a field and as an industry. In fact, we’ve barely explored and exploit Artificial Intelligence. We are still toying with deep learning models and still trying to perfect our narrow AIs.



Therefore, between ‘now’ that you are still preparing for a career in AI to the time you will be building your AI career, there would be a lot of changes/advancements; AI is changing every now and then.



Nonetheless, there are certain things about preparing for a career in AI that are kind of immune to changes/advancement. And those are the things I will dwell on in this post, shall we?

Learn Artificial Intelligence or a Related Field



One of the major (if not the major) thing employers look for in recruiting job applicants is their knowledge of the ‘void’ they intend to fill. This knowledge usually revolves around what they (the job applicants) know and what they can do (this is often referred to as hard skills).



Hence, the first thing you should do is to strive to have employable knowledge of Artificial intelligence (acquire the hard skills).

Remember, I mentioned earlier that AI is complex (broad) and as such it has many sub-fields. You can’t know everything about AI and even if you do, you will only need knowledge in at least one of its sub-fields to pursue your AI career.



AS of now the major sub-fields of AI are:

Machine Learning: A Sub-field of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

A Sub-field of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Computer Vision: A sub-field of AI that deals with how computers can be made to gain high-level understanding from digital images or videos. In essence, make computers to see (more like artificial vision).

A sub-field of AI that deals with how computers can be made to gain high-level understanding from digital images or videos. In essence, make computers to see (more like artificial vision). Evolutionary Computation: A sub-field of AI that focuses on developing a family of algorithms for global optimization, it is inspired by biological evolution. And viewed as the next step in the progress of AI.

A sub-field of AI that focuses on developing a family of algorithms for global optimization, it is inspired by biological evolution. And viewed as the next step in the progress of AI. Natural Language Processing & Planning: It’s another Sub-field of AI that focuses on the interactions between computers and human languages (natural languages), in order to enable computers process and analyze large amounts of natural language data. NLPP usually revolves around speech recognition, natural language understanding and natural language generation.

It’s another Sub-field of AI that focuses on the interactions between computers and human languages (natural languages), in order to enable computers process and analyze large amounts of natural language data. NLPP usually revolves around speech recognition, natural language understanding and natural language generation. Expert Systems: It’s a sub-field of AI that attempts to emulate the decision-making ability of a human expert by reasoning through bodies of knowledge mainly as if-else-then rules.



Note: The use/value of expert systems in present day AI is coming close to completely nothing. Mainly because of advances in deep learning (a sub-field of Machine learning).

In your attempt to gain employ worthy knowledge of Artificial Intelligence, I strongly recommend you start with machine learning and then branch off from there.

Here is an Article to guide you towards learning Artificial Intelligence.



There are two things you need to be doing after you’ve gained appreciable knowledge of Machine Learning:

You need to continue carrying out practice projects as much as you can.

And you should also consider taking on freelanced AI projects and attempt to complete them.

Remember I noted that AI as a field and as an industry is still developing. Hence, ensure you stay up-to-date as much as you can with the latest developments in the field and industry.

Hone your Soft Skills



Having knowledge of artificial intelligence is not enough to pursue and build a successful career in AI. You need to acquire ‘soft skills’ for career pursuit and development.

Soft Skills?



Yes, soft skills!



According to the Oxford Dictionary, Soft skills are personal attributes that enable someone to interact ‘effectively’ and ‘harmoniously’ with other people.



Did you noticed that I highlighted the words ‘effectively’ and ‘harmoniously’?



If you did, great and if you didn’t, good. It’s one thing to interact with people and another to do so effectively and harmoniously.



Effective interactions with people is fundamental for securing jobs and building a successful career.



According to Linkedin’s 2019 Global Talent Trends’ report, soft skills is the topmost trend reshaping recruiting and HR. In the report, 92% of talent professionals say soft skills matters as much or more than hard sills when they were hired. And 80% say they are increasingly important to company success.



Therefore, as much commitment you are giving to developing your hard skills (learning AI) should be given to developing your soft skills. Employers give as much consideration to your soft skills as they give to your hard skills when considering if you should or shouldn’t be hired.

Post-employment, soft skills becomes even more important.

A research conducted by Shaheen et al (2012) on the ‘importance of soft skills for education and career success’ finds that soft skills are useful for career advancement and that the top five important soft skills are:

Teamwork and collaboration

Decision making

Problem solving

Time Management

and Critical thinking skills

Others include:

Leadership

Improve your Employability Skills



According to Maciej Duszyisy, a resume expert at Zety, Employability skills is an umbrella term for a set of highly desirable and transferable skills that turn you into a very attractive candidate or employee.



One way to look at employability skills, is to put yourself in the shoes of your potential employer. If you are an employer, what are the qualities you expect to see in your potential employees?



Employability skills are simple skills that make you attractive for a job in the eyes of an employer.



The Canadian Corporate Council on Education in May 1992, released a publication titled ‘Employability Skills Profile’. The publication was a result of a collaboration between the Corporate Council and Employers in Canada.

It revealed that employers were looking for the following traits in potential employees:

People who can communicate, think, and continue to learn throughout their lives

People who can demonstrate positive attitudes and behaviours, responsibility and adaptability.

People who can work with others



The above employability traits/skillsets are enough in an ideal world but not entirely so for the real world.



For instance, it’s not news that there are people who are not qualified for a job, yet, they get the job on the ground of who they know

.

Ever heard of “it’s not what you know but who you know”?



Of course when people are employed simply because of who they know, it’s bad for business especially when its people who are not ‘qualified’ for the job, who tend to underperformed when hired.



But imagine, if you are qualified for a job and got the job partly because of your connections, well that’s not bad, at least for business.



Therefore, I employ you to develop your employability skills by making yourself qualified and attractive for an AI job while building a strong network of people you know.



Networking for career is vital, as it will open doors for you easily. Now, don’t view networking as knowing important people in the AI industry but rather as getting those important people to know and like you. Because networking is really not about ‘who you know’ but ‘who knows you’.



Here is an article on Zety.com on the top 10 employability skills and how you can improve on them. It’s a good place to start when you are thinking of the specifics of employability skills.



Then, in terms of networking, livecareer.com have a detailed article on how to build and expand your career network and thebalancecareers.com have another great article on how to use networking to find a job. Check them out as a starting guide towards building a strong career network.

Educational Requirement



Bachelors or Masters Degree in:

Artificial Intelligence

Robotics

Computer Science

Machine Learning

Statistics

Data Science

Mathematics

Software Engineering

Although getting a relevant degree is one of the most popular educational requirements for getting a job in the AI Industry, one can still get a job in AI without a degree.

Career Options

Data Scientist

Video Game Programmer

Robotics Programmer/Engineer

Artificial Intelligence Researcher

Data Mining Analyst

Algorithms Specialist

Machine Learning Engineer

Top AI Companies to Work in:

