How to Become an Artificial Intelligence (AI) Engineer in 2024?

7 min read

Career Trends: April 6, 2024

Curated by the Knowledge Team of  ICS Career GPS

The discipline of AI engineering is still relatively new, but it has the potential to open up a wealth of employment doors in the years to come.

AI engineers are in high demand and for good reason. Artificial intelligence has seemingly endless potential to improve and simplify tasks commonly done by humans, including speech recognition, image processing, business process management, and even the diagnosis of disease.

If you’re already technically inclined and have a background in software programming, you may want to consider a lucrative AI career and know how to become an AI engineer.

What is Artificial Intelligence?

Artificial Intelligence (AI) is a computer system’s ability to mimic human behaviour. Machines demonstrate this sort of intelligence, which can be compared to the natural intelligence that humans and animals demonstrate. 

In artificial intelligence (AI), machines learn from past data and actions, which are positive or negative. With this new information, the machine can make corrections to itself so that the problems don’t resurface, as well as make any necessary adjustments to handle new inputs. Finally, the machine can perform human-like tasks.

What Does an AI Engineer Do?

An AI engineer builds AI models using machine learning algorithms and deep learning neural networks to draw business insights, which can be used to make business decisions that affect the entire organisation. These engineers also create weak or strong AIs, depending on what goals they want to achieve. AI engineers have a sound understanding of programming, software engineering, and data science. They use different tools and techniques so they can process data, as well as develop and maintain AI systems.

AI Engineers build different types of AI applications, such as contextual advertising based on sentiment analysis, visual identification or perception and language translation.

Artificial Intelligence Qualification/Education

The discipline of AI engineering is still relatively new, but it has the potential to open up a wealth of employment doors in the years to come. A bachelor’s degree in a relevant subject, such as information technology, computer engineering, statistics, or data science, is the very minimum needed for entry into the area of artificial intelligence engineering.

A master’s degree in artificial intelligence may be pursued after earning a bachelor’s degree in computer science. Having credentials in data science, deep learning, and machine learning may help you get a job and offer you a thorough grasp of essential subjects.

Skills Required to Become an AI Engineer

Professionals seeking to become an AI engineer should also know about the skills required in this field. Some of them include:

1. Technical Skills
  • Programming Skills 

The first skill required to become an AI engineer is programming. To become well-versed in AI, it’s crucial to learn programming languages, such as Python, R, Java, and C++ to build and implement models. 

  • Linear Algebra, Probability, and Statistics 

To understand and implement different AI models—such as Hidden Markov models, Naive Bayes, Gaussian mixture models, and linear discriminant analysis—you must have detailed knowledge of linear algebra, probability, and statistics.

  • Spark and Big Data Technologies 

AI engineers work with large volumes of data, which could be streaming or real-time production-level data in terabytes or petabytes. For such data, these engineers need to know about Spark and other big data technologies to make sense of it. Along with Apache Spark, one can also use other big data technologies, such as Hadoop, Cassandra, and MongoDB.

  • Algorithms and Frameworks 

Understanding how machine learning algorithms like linear regression, KNN, Naive Bayes, Support Vector Machine, and others work will help you implement machine learning models with ease. Additionally, to build AI models with unstructured data, you should understand deep learning algorithms and implement them using a framework. Some of the frameworks used in artificial intelligence are PyTorch, Theano, TensorFlow, and Caffe.

2. People Skills 

The difference between successful engineers and those who struggle is rooted in their soft skills. Although being an AI engineer is primarily a technical job, the ability to effectively communicate with others, deal with problems, organise one’s time effectively, and work in collaboration with others are the key factors that determine whether or not a project is completed and delivered.

  • Communication Skills

There is a broad range of people with different levels of competence that artificial intelligence engineers have to talk to. Suppose that your company asks you to create and deliver a new artificial intelligence model to every division inside the company. If you want to convey complicated thoughts and concepts to a wide audience, you’ll probably want to brush up on your written and spoken communication abilities.

  • Problem-solving Abilities

To be a successful data scientist or software engineer, you must be able to think creatively and solve problems. Because artificial intelligence seeks to address problems as they emerge in real time, it necessitates the development of problem-solving skills that are both critical and creative.

  • Domain Expertise

Engineers in the field of machine learning must recognise both the demands of the company and the sorts of obstacles their designs are addressing to create self-running programs and optimise solutions utilised by organisations and customers. A lack of expertise in the relevant field might lead to inaccurate suggestions, work that is incomplete, and a model that is difficult to assess.

  • Time Management

Engineers in the field of artificial intelligence must balance the needs of several stakeholders with the need to do research, organise and plan projects, create software, and thoroughly test it. The ability to effectively manage one’s time is essential to becoming a productive member of the team.

  • Teamwork

Members of the AI and IT communities often collaborate. The ability to operate successfully and productively in a team is a valuable skill to have. You may be required to work with both small and big groups to accomplish complicated objectives. Taking into account the opinions of others and offering your own via clear and concise communication may help you become a successful member of a team.

  • Business Intelligence

The majority of problems relating to the management of an organisation may be resolved by means of successful artificial intelligence initiatives. If you have business intelligence, you will be able to transform your technological ideas into productive commercial ventures. You may strive to establish a fundamental grasp of how companies function, the audiences they cater to, and the rivalry within the market, regardless of the sector in which you are currently employed.

  • Critical Thinking Ability 

Creative AI models and technology solutions may need to come up with a multitude of answers to a single issue. You would also have to swiftly evaluate the given facts to form reasonable conclusions. You can acquire and strengthen most of these capabilities while earning your bachelor’s degree, but you may explore extra experiences and chances to expand your talents in this area if you want to.

How to Build a Career in AI

In addition to learning how to become an AI engineer, it is important to also understand which industry you want to work with. Since several industries around the world use AI to some degree or other, including healthcare and education, there has been exponential growth in the career opportunities within the field of AI. Some of these job roles are:

1. AI Developer 

An AI developer works closely with electrical engineers and develops software to create artificially intelligent robots.

2. AI Architect 

AI architects work closely with clients to provide constructive business and system integration services. They also create and maintain the entire architecture.

3. Machine Learning Engineer 

Machine learning, or ML engineers build predictive models using vast volumes of data. They have in-depth knowledge of machine learning algorithms, deep learning algorithms, and deep learning frameworks.

4. Data Scientist 

Data scientists collect, clean, analyse, and interpret large and complex datasets by leveraging both machine learning and predictive analytics.

5. Business Intelligence Developer 

They’re responsible for designing, modelling, and analysing complex data to identify business and market trends.

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(Disclaimer: The opinions expressed in the article mentioned above are those of the author(s). They do not purport to reflect the opinions or views of ICS Career GPS or its staff.)

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