Artificial intelligence is approaching real-life with applications closer than ever.
The field has a high potential for growth as these technologies become more common. AI job prospects are, therefore, desirable.
If you are looking for how to become an AI engineer, this article is for you. Read on to discover this profession’s concepts, processes, and skills.
Artificial intelligence (AI) is intelligent computer systems capable of learning and mimicking human behaviors. Its goal is to gain the ability to reason, perceive, and learn to solve problems.
AI combines the theory and development of computer systems to perform tasks requiring human intelligence. Such tasks include text and speech recognition, translation, and visual perception.
In computer science, the term artificial intelligence is also a concept that refers to the brightness of machines. It is a different category from the innate intelligence of animals and humans.
Machines are capable of learning and copying data from past actions and inputs. These include both negative and positive experiences.
Every time machines receive new data or information. They improve themselves to form a reflex. The primary purpose is to keep the device from making the same old mistakes.
Over time, this ability to learn and practice enables machines to do the same things as humans.
Artificial intelligence engineers are responsible for developing models, programs, and algorithms for deep learning neural networks.
These experts research and create powerful technologies for learning and solving tasks, analyzing trends, and identifying future events.
AI engineers research and create their machine learning models through diverse data sources.
They implement the model using API calls or embed code to create AI-related applications.
Depending on the position and role of the specialist, the task list will include:
- Creation of machine learning algorithms.
- Develop and test models.
- Team management.
- Communicate stakeholders to discuss the project.
- Automate infrastructure and processes.
- Software development.
- Analyze and track progress and metrics.
- Developing machine learning algorithms.
Engineers will work directly with machine learning scientists, business analysts, and data scientists. The most popular programming language is Python.
Some popular platforms that professionals use are GitHub Copilot, Amazon Web Services, Google’s Vertex AI, and IBM Watson Studio.
There are many ways for artificial intelligence engineers to develop and practice their capabilities. Yet, there is specific knowledge and skills that you have to add.
The field of artificial intelligence engineering requires professionals with a wide range of skills and educational backgrounds.
The essential degree in most occupations is a bachelor’s degree, but the field of study required is very flexible. Some popular majors are computer science, computer engineering, information systems, and data science.
You can study any major, but make sure the Accreditation Board accredits them for Engineering and Technology (ABET).
Besides participating in regular courses, you can choose boot camps to practice skills.
The duration of these programs is relatively concise. The content focuses mainly on practice instead of theories.
By self-learning, you can also develop your own AI skills. Besides, keep in mind that popular programming languages include:
Employers will prefer candidates who have invested in diverse portfolios.
You can complete projects and class assignments even if you haven’t joined any organization or company. Use them as much as you can to highlight your skills.
However, you should demonstrate social and behavioral abilities.
When choosing projects to add to your portfolio, keep an eye out for projects that are complex and particularly interesting, it will make your resume more attractive than a chaotic arrangement.
For students majoring in engineering, an internship can be a rewarding experience.
The significant benefit is that you can work in a professional and practical environment. The projects here are also suitable to highlight the portfolio.
Students can benefit significantly from networking and contacting various organizations. They will even get a full-time position at the end of the internship.
Depending on each individual’s goals and a long-term orientation, you can choose the job accordingly.
Some techniques students should focus on to develop include cybersecurity, information technology, or automotive systems. Concentrating your energy on a specific industry will help you grow faster than ever.
As the number of AI applications increases, so will the demand for careers from organizations using this technology.
Besides information technology, professionals can find work in other industries such as business, transportation, and healthcare.
The job characteristics of engineers in each industry will be different. AI specialists take on diverse responsibilities even if they are in the same position.
A few have expertise in business intelligence and machine learning interactions. Meanwhile, others focus on the research aspect and predictive analytics.
For jobs that require a lot of technical expertise like AI engineers, professional certifications will help a lot.
The prerequisite is that you need to choose to study certificates from reputable and verified centers. They guarantee formal training and provide you with the necessary documentation.
To participate in these certifications, you need to meet several entry requirements related to experience and qualifications.
Some professional centers may even require students to take an entrance exam before starting. So, carefully check the certifications you want to study.
To make your resume more attractive, you need a proper method of promotion.
The simplest method is to keep things as simple and concise as possible. The maximum recommended size is two pages.
AI pros must refine their resumes to ensure employers and organizations notice them first. Pay attention to your ability compared to the position you are applying for.
As someone specializing in technical work, pay attention to emphasizing relevant skills. Some special notes are algorithms, programming languages, and data analysis knowledge.
The interview process and questions will vary from organization to organization. A candidate’s experience is a notable factor.
In addition, recruiters also add algorithmic and coding questions to ensure that the right candidate is found.
Some essential skills that AI engineers can learn and hone include technical and non-technical engineering. These include:
Technical skills are skills that certainly need to be covered by professionals. You can learn them through training courses or boot camps.
To deploy machine learning models, experts need to be fully equipped with knowledge of machine learning algorithms.
These include KNN, Support Vector Machine, Linear Regression, and Naive Bayes. These programs will be indispensable in your work.
When using unstructured data to build AI models, you will need deep learning algorithms to make the job easier.
Deploying algorithms such as generic adversarial networks and cyclic neural networks will require the support of a framework.
Examples of popular frameworks in artificial intelligence are TensorFlow, PyTorch, Caffe, and Theano.
Programming is definitely one of the vital skills for technology engineers.
Programming language is the most basic requirement for getting acquainted with artificial intelligence and model building.
The amount of data that artificial intelligence professionals contact daily can reach terabytes, or even petabytes, in real-time.
Much of this data is live or real-time production-grade data. In that case, they need to be equipped with knowledge of big data technologies, such as Spark.
Some other examples of this type of technology that you can refer to are MongoDB, Hadoop, and Cassandra.
These insights give professionals the ability to implement and understand different AI models. Besides, you learned more knowledge like probability statistics and linear algebra.
While engineering-related skills are essential in assessing an AI engineer’s ability to get the job done, they are not enough.
A prerequisite for a professional developing and achieving much in his career is a complete set of soft skills.
The ability to communicate and work in a team are indispensable links to determine the project’s outcome.
Creative thinking and problem-solving are essential skills for data scientists to become successful.
The problems that artificial intelligence can solve often appear in real-time. Therefore, these professionals need to be able to solve problems creatively, quickly, and accurately.
The AI and IT team members will cooperate with each other. Teamwork and project management skills are essential to accomplishing small or large goals.
You should learn how to communicate with people as quickly and concisely as possible. Besides, please open your heart and accept comments most openly.
A pleasant attitude and a spirit of cooperation will make the work atmosphere lighter. From there, you can speed up the overall workflow more.
You cannot get the job done without smooth and effective communication. All employees in the company with many levels of power need to talk to each other.
The artificial intelligence industry is a rather specific industry with many complex terms. Those who do not work in the industry will undoubtedly find it difficult to understand them.
Therefore, you need good communication skills to convey concepts and thoughts to different audiences.
Take note of both oral and written communication and communication styles. They will be helpful depending on the circumstances.
The goal that experts in deep learning and machine learning should keep in mind is to identify the current challenges and needs of the organization.
From there, they can research and create intelligent programs that operate independently to solve and optimize performance for institutional customers.
You will find it challenging to work without expertise and skills. The main reason is that it will affect the accuracy of the recommendation as well as the quality of the model.
AI-related inventions or innovations are often capable of effectively solving business problems.
However, it will be a problem if you want to make your idea or project stand out in the market. That time will require you to supplement your knowledge of business intelligence.
No matter what industry you work in, it would be best if you still had a basic understanding of the company’s target audience, products, and direction of growth.
There will be a lot of tasks that require artificial intelligence experts to complete. You probably won’t have time to get everything done.
Some of the work you need include project planning, testing, and software creation. Make sure you can balance your schedule between work and life.
This good habit will help you achieve greater efficiency at work and when participating in projects.
In the US, the average salary of an AI engineer is around $150,000. Compared to the average salary of a data scientist, $130,000 is much higher.
This salary is relatively high compared to other information technology professions because of the hot job demand.
According to BLS, job demand for information technology industries will increase by 13% in the ten years from 2020 to 2030.
Cloud computing, big data, and information security trends will drive career demand to grow faster than usual.
The explosive growth of AI and machine learning-related applications has made the career demand of AI engineers more than ever before.
If you are planning to participate in this work, please pay attention to the road map that includes the following steps:
- Learn skills.
- Take on projects.
- Pursue internship.
- Determine your AI scientist career path.
- Earn AI engineer certifications.
- Create a resume and apply for your dream jobs.
You will definitely need to spend a long time completing this process. However, the results are entirely worth it.
Start referring to the basics and join the courses right now. Time does not wait, and you need to seize the opportunity as soon as possible.