The Road Map That Turns You Into An AI Engineer

The Road Map That Turns You Into An AI Engineer

Investing In AI-Related Skills Opens Up Many Career Opportunities In Various Industries.

Industries and fields that use artificial intelligence capabilities to increase automation and improve productivity. Statistics show that artificial intelligence has become an essential and influential lever in software companies, commercial activities, and manufacturing services.

The road map that turns you into an AI engineer

A report published in February 2021 by Consulting Firm and International Data Com shows that the financial turnover in the artificial intelligence market in software, hardware, and services has reached 327.5 billion dollars, which offers a 16% growth compared to 2020.

By 2024, the artificial intelligence market is expected to reach 500 billion dollars. This report is prepared that the demand for this field in various industries will grow by 18% every year, so many sectors will use machine learning, machine vision, and interactive artificial intelligence extensively.

Along with the expansion of artificial intelligence in various industries, the need for professionals with fake intelligence skills will increase. A report published by the job search site Lakedin in 2020 showed that artificial intelligence is still at the top of the list of jobs.

Among the job positions that can play an important role in artificial intelligence strategies in different organizations is artificial intelligence engineer .

Some Algorithms can act like the human brain. Specialists in this role are responsible for developing, planning, and training complex networks and algorithms whose origin is artificial intelligence. A report published by the added job site shows that this position combines software development, programming, data science, and data engineering skills.

  • Artificial intelligence engineers are mainly tasked with using different algorithms, frameworks, and programming languages ​​to build, test, and use artificial intelligence models. Indeed, in this regard: “Other duties usually mentioned in the description of the artificial intelligence engineer job title include coordinating with other team members, building and managing the artificial intelligence development process, performing statistical analysis and interpreting the results. It should note that the work output can play a positive role in the organizational decision-making process. In addition, the AI ​​engineer should help the organization automate critical infrastructure for data science teams.” Additional responsibilities include building the infrastructure for data transformation and use. One of the essential skills that an AI engineer must have is the ability to explain the functionality and usefulness of AI models to a wide range of people within the organization, including stakeholders, CEOs, and product managers. 
  • Another critical skill an AI engineer should have is the ability to transform machine learning modes into functional programming interfaces with which other programs can interact.
  • Since artificial intelligence and machine learning significantly impact an organization’s success, so artificial intelligence engineers are essential for organizations. Advanced machine learning models provide valuable recommendations and insights for future issues or decisions.
  • In addition to the skills mentioned above, a machine learning engineer should clearly understand popular programming languages ​​such as C++, Java, R, and Python, statistics, probability, and linear algebra, and analytical skills. Have. 
  • In addition, it is necessary to have sufficient knowledge about soft skills such as a strong understanding of business, good communication, critical thinking, and cooperation with other members.

What are the primary duties of an artificial intelligence engineer?

Typically, companies expect an AI engineer to research suitable machine learning algorithms, modify data science prototypes, know how to develop machine learning programs based on requirements, collaborate with electrical and robotics engineers, test learning, Run the machine, train the systems if necessary, and select appropriate data visualization techniques and datasets.

Does an AI specialist need a college degree?

Considering that most of the topics that artificial intelligence focuses on are mathematics, programming, statistics, and probability and logic (discrete mathematics), it is evident that a university graduate is a step ahead of the average person. Still, a university degree is an It is not a complete necessity. Typically, an AI specialist with a bachelor’s degree in computer science, information technology, mathematics and statistics, finance, or economics will become an AI specialist in the shortest time.

In addition to a bachelor’s degree, you must be proficient in analytical skills, problem-solving skills, creative thinking, effective communication, and industry knowledge. These skills are acquired through online courses or a master’s degree.

However, since AI is The keyword in the world of technology today, we suggest you take online courses and training in reputable institutions. One thing to note about a bachelor’s degree is that if you want to take on AI team leadership and supervision roles, you must have a master’s or Ph.D.

A master’s degree in computer science specializing in artificial intelligence or a master’s in artificial intelligence allows you to build your career in artificial intelligence. The master’s program generally focuses on developing professionals and has intense courses that address real-world problems and applied areas.


What technical skills are needed?

Programming skills are essential principles you must have as an AI engineer. An artificial intelligence specialist should have sufficient knowledge of programming languages ​​such as R, Python, Java, C++, and MATLAB. Proficiency in programming languages such as R, Python, Java, C Plus Plus, and MATLAB is necessary.

This topic helps in understanding concepts like data structures and classes. Learning more than one programming language is always an added advantage as organizations are focused on multi-skilled people these days.

Statistics, probability, and linear algebra 

You need to know statistics to understand how algorithms and machine learning functions work. Also, have enough information about Gaussian distributions, standard deviation, and mean. Therefore, you must have sufficient knowledge about probability and models such as Naive Bayes, hidden Markov models, and Gaussian mixture models, and be familiar with the principles related to vectors, matrices, matrix multiplication, understanding integrals, and derivatives.

Natural Language Processing

Natural language processing is an essential subfield of artificial intelligence. This technology enables computers to understand and process human language. In short, natural language processing makes computers come close to understanding human language. The two main areas covered by this technology are linguistics and computer science.

An artificial intelligence engineer in natural language processing must have sufficient knowledge about the topics of the streaming world, such as audio, image, and text. For this reason, it is necessary to have control skills and sufficient knowledge in libraries such as NLTK and Gensim and techniques such as sentiment analysis, summarization, and word2vec.

Non-technical skills

How can an AI engineer explain the importance of AI to stakeholders and non-technical people? To become a successful AI engineer, knowing non-technical fields is essential. So you should have in-depth knowledge about the industry you are planning to enter, as this will help you understand the real-world scenarios and industry weaknesses.

In addition, you should focus on communication skills. Concepts such as artificial intelligence and machine learning are difficult to explain to someone with poor communication skills.

Therefore, when you communicate well, you can better explain such concepts to people with little technical knowledge. Another essential concept in this field is critical thinking.

Numbers and data should be fascinated by artificial intelligence, and they must be able to use these findings and draw clear conclusions, which is possible with critical thinking. With the rise of artificial intelligence, every sector is looking to implement artificial intelligence, doubling the demand for experts.