Artificial Intelligence Engineering

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 organization. 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.

The next section of How to become an AI Engineer focuses on the responsibilities of an AI engineer.

What does it take to be an AI engineer?

AI engineering is a relatively new field, and those who currently hold this title come from a range of backgrounds. The following are some of the traits that many have in common.

Education

Many AI engineers moved over from previous technical roles and often have undergraduate or graduate degrees in fields that are required for those jobs. These include:

• Computer science
• Statistics
• Applied mathematics
• Linguistics
• Cognitive science

Most of the above degrees have some relevance to artificial intelligence and machine learning.

Technical skills

Two of the most important technical skills for an AI engineer to master are programming and math/statistics.

• Programming: Software developers moving into an AI role or developers with a degree in computer science likely already have a grasp on a few programming languages. Two of the most commonly used languages in AI, and specifically machine learning, are Python and R. Any aspiring AI engineer should at least be familiar with these two languages and their most commonly used libraries and packages.
• Math/statistics: AI engineering is more than just coding. Machine learning models are based on mathematical concepts like statistics and probability. You will also need to have a firm grasp on concepts like statistical significance when you are determining the validity and accuracy of your models.

Soft skills

AI engineers don’t work in a vacuum. So while technical skills will be what you need for modeling, you’ll also need the following soft skills to get your ideas across to the entire organization.

• Creativity – AI engineers should always be on the lookout for tasks that humans do inefficiently and machines could do better. You should stay abreast of new AI applications within and outside of your industry and consider if they could be used in your company. In addition, you shouldn’t be afraid to try out-of-the-box ideas.
• Business knowledge – It’s important to remember that your role as an AI engineer is meant to provide value to your company. You can’t provide value if you don’t really understand your company’s interest and needs from a strategic and tactical level.

A cool AI application doesn’t mean much if it isn’t relevant to your company or can’t improve business operations in any way. You’ll need to understand your company’s business model, who the target customers and targets are, and if it has any long- or short-term product plans.

• Communication – In the role of an AI engineer, you’ll have the opportunity to work with groups all over your organization, and you’ll need to be able to speak their language. For example, for one project you’ll have to: 

• Discuss your needs with data engineers so they can deliver the right data sources to you.
• Explain to finance/operations how the AI application you’re developing will save costs in the long run or bring in more revenue.
• Work with marketing to develop customer-focused collateral explaining the value of a new application.
• Prototyping – Your ideas aren’t necessarily going to be perfect on the first attempt. Success will depend on your ability to quickly test and modify models until you find something that works.

What Does It Take To Become an AI Engineer?

To become an expert in artificial intelligence, you will need to follow these steps:

• Obtain a Secondary School Diploma

The first need to fulfill in order to enter the field of artificial intelligence engineering is to get a high school diploma with a specialization in a scientific discipline, such as chemistry, physics, or mathematics. You can also include statistics among your foundational disciplines in your schooling. If you leave high school with a strong background in scientific subjects, you’ll have a solid foundation from which to build your subsequent learning.

• Complete a Bachelor’s Degree

You can enroll in a Bachelor of Science (B.Sc.) program that lasts for three years instead of a Bachelor of Technology (B.Tech.) program that lasts for four years. It is also possible to get an engineering degree in a conceptually comparable field, such as information technology or computer science, and then specialize in artificial intelligence alongside data science and machine learning. To get into prestigious engineering institutions like NITs, IITs, and IIITs, you may need to do well on the Joint Entrance Examination (JEE).

• Pursue a Master’s Degree

To give yourself a competing chance for AI engineering careers and increase your earning capacity, you may consider getting Artificial Intelligence Engineer Master’s degree in a similar discipline. It might provide you with a comprehensive understanding of the topic as well as specialized technical abilities. You may be required to take the GATE exam in order to enroll in an engineering program.

• Earn Certifications

On the other hand, participating in Artificial Intelligence Courses or diploma programs may help you increase your abilities at a lower financial investment. There are graduate and post-graduate degrees available in artificial intelligence and machine learning that you may pursue. You may get online certifications at your own speed via a variety of platforms, such as Simplilearn, which provides online training courses.

• Seek Employment

After you have obtained a sufficient amount of expertise in the subject, you may begin to apply for positions in the disciplines of artificial intelligence (AI), deep learning, and machine learning. In this industry, there is a wide variety of job types available, including data scientist, AI expert, machine learning developer, ML engineer, robotics engineer, and data scientist. You have the option to begin your career as an employee in a lower-level job and then work toward advancing to positions of more responsibility as your expertise grows.

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.

Responsibilities of an AI Engineer

As an AI engineer or an ML engineer, you need to perform certain tasks, such as develop, test, and deploy AI models through programming algorithms like random forest, logistic regression, linear regression, and so on.

Responsibilities include:

• Convert the machine learning models into application program interfaces (APIs) so that other applications can use it
• Build AI models from scratch and help the different components of the organization (such as product managers and stakeholders) understand what results they gain from the model
• Build data ingestion and data transformation infrastructure
• Automate the infrastructure that the data science team uses
• Perform statistical analysis and tune the results so that the organization can make better-informed decisions
• Set up and manage AI development and product infrastructure
• Be a good team player, as coordinating with others is a must

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:

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 (like a convolutional neural network, recurrent neural network, and generative adversarial network) and implement them using a framework. Some of the frameworks used in artificial intelligence are PyTorch, Theano, TensorFlow, and Caffe.

Soft 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, organize one’s time effectively, and work in collaboration with others are the key factors that determine whether or not a project is successfully 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 recognize both the demands of the company and the sorts of obstacles their designs are addressing in order to create self-running programs and optimize solutions utilized by organizations and customers. A lack of expertise in the relevant field might lead to suggestions that are inaccurate, 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, organize 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 with one another. 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 organization 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 for extra experiences and chances to expand your talents in this area if you want to.