Best ways to Build a Career in Artificial Intelligence

Ways To Build A Career In Artificial Intelligence

What runs in your mind when you hear the word Artificial Intelligence? You will probably think of robots, chatbots, remote control cars, or even self-driving cars. You are not wrong; these are a few examples of artificial intelligence.

The growth of Artificial intelligence (AI) is not as steady as the growth of other technologies. However, many CEOs and business leaders believe AI still has a lot to offer.

A report by Turing’s paper in 1950 foresaw the goal and future of AI. Many organizations are embracing AI, and this means that the need for professionals in this sector will increase in the coming years.

Just as technology changes, there is a need for academic and training programs to offer skills that sync with the current trends in the industry. Organizations and employees need to be flexible, so they are able to adapt their skills to meet current demand. Here is a guide that will be helpful for you to build a career in AI.

As a starter, you require a bachelor’s degree in any of these fields; Computer Science, Information Technology, Finance, Economics and Mathematics, and Statistics. Additionally, you should be proficient in creative thinking, analytical skills, problem-solving, effective communication, and be knowledgeable about the industry.

To become an engineer in AI, you should equip yourself with knowledge about the latest technologies as well as fine-tuning your technical skills.

Required skills:

Artificial Intelligence is an emerging technology; many professionals are running to this field. Professionals looking to enter this field should have a closer look at the business, and the technical skills they need to master to become successful in the industry.

1. Technical skills:

  • Programming languages: These include Java, C++, R, and Python. Being proficient in programming languages is very important. Having knowledge of coding will help you to understand concepts such as data structures and classes. It is an added advantage to learn more than one programming language.

  • Statistics, Calculus, and Linear Algebra: It is crucial to understand how machine learning and algorithms function. Standard deviations, mean and Gaussian are the most common terms that always come up.

    It is advisable to have a solid knowledge of models such as Hidden Markov models, Naïve Byes, and Gaussian Mixture as well as a proper understanding of probability. Matrices, vectors, matrix multiplication, and knowledge of derivatives and integrals are essential as well.

  • Natural Language Processing: This is a subfield of AI, which enables computers to understand and process human languages. It covers two significant areas of work, computer science, and linguistics. Audio, text, or video can be a part of this too. It is imperative to have excellent working knowledge in libraries, and summarization.

  • Neural Network Architectures: Machine learning comes to play when tasks get too complicated and complex for humans to code. The recent advances in the machine learning field enable computers to perform tasks that are impractical for the human mind to understand.

    Neural networks prove to be the precise method of approaching problems such as image classification, recognition of speech, and translation.

2. Non –Technical skills:

Artificial Intelligence can be useful in explaining certain critical events to an organization’s stakeholders. Therefore, you will also require non-technical skills to be a successful AI engineer. There are many artificial intelligence courses available online to equip you with the skills to understand the business implications of these technologies.

  • Communication Skills: AI engineers need to be fluent communicators. Explaining concepts like machine language and AI can be tough if the communicator does not do so in a simple language. Remember, you are demonstrating these concepts to individuals with very little or no technical knowledge.
  • Industry Knowledge: It is crucial to keep track of industry trends to remain competitive in the market.
  • Critical Thinking: AI engineers live in a world of numbers and data, and they should be able to use these findings to get useful conclusions. The emergence of AI in this industry increases the demand for professionals with skills and knowledge in these areas.

Having all the required certification and skills is not enough, you need to build your career in AI, and these steps will help in your career building.

Navigate the landscape

There’s a lot of career scope in Artificial Intelligence, and a person cannot be an expert in all domains. Various skills are essential in AI projects and you need to understand where your interest lies and specialize in that.

Work on building blocks

Understanding of the concepts, processes, and tools are an essential part of any task. You will need to understand what scope of the AI field requires what kind of skill.

Utilize resources

There are many reading materials, blogs, data sets, and portals. Make use of these materials to get more exposure and evaluate the resources.

Build your portfolio

A credible portfolio is useful in showcasing your talent and skill. It would be best if you built a portfolio, which is impressive before you enter the market.

Network and land the job

You need to have professionals in your network that are aware of your capabilities and can refer you to jobs that match your skills.

Future-proof your career

Take the extra steps and prepare yourself for technology disruptions and make sure you can adapt to those changes to secure your future career.


There has been an emergence of online programs and training to meet the increasing need for professionals who require skills in AI certification as well as Machine learning certification. Learning levels and candidate requirements are the primary tools in deciding which level of certification one can acquire from a reputed certification body. Always ensure that a certification program has a global validation before selecting it.

Author Bio:

Emma is a passionate writer & guest blogger. Writing helps her to improve her knowledge, skills & understanding about the specific industry. She loves writing & sharing her knowledge mostly in career & education niche. She is passionate about spreading her knowledge & tips across the world. Apart from writing, she loves traveling, styling & cooking. You can follow her using her LinkedIn Profile 

Guest Author SoftwareTestingMaterial This post is written by the above mentioned Guest Author.

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