Things to know to be an AI engineer

AI engineer
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Artificial Intelligence is among the largest technological wave, which has hit the world of tech. The global AI market will grow at a rate of 154 percent. This resulted in the high demand for AI engineers today.

According to research done by Gartner about Artificial Intelligence:

  • AI will create a business value worth US$3.9 trillion by 2022.
  • It is estimated that AI will be the most disruptive technology in the coming years due to the advances in computing power, capacity, speed, and data diversity, and progresses in Deep Neural Networks (DNN).
  • Decision automation systems – one of the fastest systems that leverage AI to automate business processes or tasks, which include translating voice, classify data that cannot be easily classified by conventional systems, etc. will grow to 16 percent by the next 4–5 years, which will be a staggering jump of 14 percent!

Who is an Artificial Intelligence Engineer?

Artificial intelligence engineers or AI engineers are people who work on artificial intelligence technology. They create, test, and implement AI models, along with the handling of algorithms, neural networks of the AI infrastructure. They also leverage the traditional software models and various other Machine Learning (ML) models. With the development of Artificial Intelligence and Machine Learning in different areas of the IT industry, the demand for these AI professionals is very high.

What are the roles and responsibilities of an AI Engineer?

Based upon their experience, knowledge of the domain they work for AI engineers are required to perform certain tasks in general. Those responsibilities include:

  • Study and transform data science prototypes
  • Develop the machine learning applications into application program interfaces (APIs) according to requirements so that other applications can use them.
  • Build AI models from scratch and help the product managers and stakeholders of the company. Explain to them the results they can attain from the model.
  • Research and implement appropriate ML algorithms and AI tools
  • Build data ingestion and data transformation infrastructure
  • Set up and manage AI development and product infrastructure
  • Stay updated with the latest developments in the field
  • Be a good team player, it is very important to coordinate with others.

There are various ways one could be an AI engineer. So, let’s understand how to become an AI engineer.

What are education prerequisites?

There are a few education prerequisites, which an individual must have to become an AI engineer. Here are those:

  • It is required to earn a bachelor’s degree first. Which can be from either of the following areas or subjects:
  • Computer Science
  • Mathematics
  • Information Technology
  • Statistics
  • Finance
  • Economics
  • Doing a master’s degree will be of great benefit too. Though it is not mandatory. One can do their master’s in one of the following:
  • Computer Science
  • Mathematics
  • Cognitive Science
  • Data Science
  • Machine Learning
  • If one has completed their bachelor’s and wants to be an AI engineer then there is another way. It is by doing a top AI certification that is well-recognized by various industries. Individuals who have a master’s degree can also do these certifications. As the certifications add a greater value to an individual’s resume and will also help them to gain an in-depth knowledge of various AI topics both theoretically and practically. One can do certifications in any of the following areas:
  • Data Science
  • Machine Learning
  • Deep Learning

Which technical and non-technical AI engineer skills are required?

To become an artificial intelligence engineer, it is important to have a few technical and non-technical skills. Mastering these skills will increase the scope of getting a good opportunity in a reputed organization. Let’s have a brief look at what these skills are:

· Technical skills

Programming languages

  • Python
  • Java
  • C++
  • Lisp
  • R
  • Prolog
  1. Linear algebra/calculus/statistics/probability
  2. Applied mathematics, frameworks, and algorithms
  3. Natural Language Processing (NLP) libraries and tools
  • NTLK
  • Sentiment Analytics
  • Gensim
  • word2vec
  • TextBlob
  • CoreNLP
  • PyNLPI

Neural Networks

·  Non-technical skills

  • Communication skills
  • Creative and critical thinking
  • Industry knowledge
  • Rapid prototyping
  • Business acumen
  • Analytical skills
  • Problem-solving skills

If one wants to enhance their skillset then doing a top AI certification will be helpful.

End Notes

The best time to get started on your dreams is now. So don’t wait anymore, start your journey to be an AI engineer by enhancing your skills with the right kind of certification and land up a dream job.

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