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

Introduction

There is no denying that artificial intelligence (AI) engineers are in high demand in present times, and that too for a good reason. Artificial intelligence has endless potential to improve and simplify various manual tasks. This includes but is not limited to image processing, speech recognition, business process management, and even the diagnosis of a deadly disease. 

If you are a student already inclined towards such technologies and have a background in software programming, you may consider pursuing an effective AI Certification course which is globally recognized, prepare for a lucrative AI career and learn about how to become an AI engineer. The following guide talks about learning how to become an artificial intelligence engineer after the 12th, including the relevant aspects associated with it. 

What is Artificial Intelligence?

So, what is artificial intelligence, or AI? AI enables various computers and machines to simulate human intelligence and engage in several problem-solving capabilities. It means AI can perform tasks on its own or combined with other technologies that previously required human intelligence or intervention. 

Autonomous vehicles, GPS guidance, digital assistants, and generative AI tools are just a few examples of AI in our daily lives. Artificial intelligence is important because it is an inevitable field of computer science. It encompasses two important elements: machine learning (ML) and deep learning. These important disciplines involve the development of various AI algorithms that work like the decision-making processes of the human brain. These algorithms can also ‘learn’ from available data and make more accurate classifications or predictions.

Artificial intelligence has gone through multiple changes over the years. However, the release of ChatGPT marked a turning point in its usage. The breakthroughs are already seen in computer vision because of the same, where the present leap forward is in natural language processing (NLP). 

Today, generative AI has developed to the extent that it can learn and synthesize human language easily. The same technology can also analyze other data types, including software code, images, video, molecular structures, etc. To identify this better, you must learn how to become AI developer or engineer, which we will eventually discuss below

What does an AI Engineer do?

If you want to learn how to become artificial intelligence engineer, then you must know about the kind of work the professional does across organizations. This includes the following:

• An AI engineer uses machine learning (ML) algorithms and deep learning neural networks to build different AI models. This helps them draw relevant business insights used to make business decisions that affect the entire organization. 
• These engineers create weak or strong AIs, which often depend on what goals they want to achieve over time. 
• AI engineers leverage programming, software engineering, and data science technologies to enhance their operations. 
• They process data using the latest tools and techniques and develop and maintain all relevant AI systems.
• AI Engineers further build different AI applications, like contextual advertising, visual identification, perception, and language translation.

How to Become an Artificial Intelligence Engineer?

Becoming an AI engineer can help you earn high salaries as an AI engineer salary is pretty lucrative.You need to follow some significant steps if you want to learn how to become artificial intelligence engineer after 12th. These include:

1. Obtain a Secondary School Diploma

Get a high school diploma with a specialization in a scientific discipline, such as physics, chemistry, or mathematics. This is the first requirement you need to fulfill to enter the field of artificial intelligence engineering. You can also include statistics as one of your foundational disciplines during school. You will always have a solid foundation from which to build your subsequent learning if you leave high school with a strong background in scientific subjects. 

2. Complete a Bachelor's Degree

Enroll in a Bachelor of Science (B.Sc.) program for three years. You can do this course instead of a Bachelor of Technology (B.Tech.) program that usually lasts for four years. Alternatively, try getting an engineering degree in a conceptually comparable field, like information technology or computer science. Later, you can specialize in artificial intelligence alongside a data science course with machine learning through various courses. 

3. Pursue a Master's Degree

You may consider getting an Artificial Intelligence Engineer Master's degree to give yourself a competitive chance for an AI engineering career. This also helps increase your earning capacity. It might provide you with a better understanding of the specific topic as well as other specialized technical abilities.

4. Earn Certifications

You may try to pursue Artificial Intelligence Courses or diploma programs to increase your abilities at a lower financial investment. Try doing some graduate and postgraduate degrees in artificial intelligence and machine learning that may also grab your attention. 

5. Gain Practical Experience

You must acquire practical experience to become an AI engineer. Of course, a strong foundation in statistics, mathematics, and computer science is always essential. However, you also need hands-on experience with real-world problems to understand the nuances of artificial intelligence. 

You can further develop practical skills and gain experience with several tools and technologies in AI engineering. This can be done through projects and participation in hackathons. Moreover, you may try pursuing online courses and bootcamps for structured learning and mentorship. This further gives you the opportunity to work on real-world projects. Eventually, you may also receive feedback from industry professionals. 

Different Fields of Artificial Intelligence

There are several techniques in the domain of artificial intelligence, like linguistics,  robotic process automation, natural language processing, bias, vision, planning, decision science, etc. Below, we acquire information about some of the major fields in AI.

1. Machine Learning

Machine Learning gives computers the potential to learn without being programmed; it is actively used in daily life without even knowing it. Fundamentally, it is the science that enables machines to translate, execute, and investigate data to solve real-world problems.

2. Neural Network

Incorporating cognitive science and machines to perform tasks, the neural network is a branch of artificial intelligence that makes use of neurology ( a part of biology that concerns the nerve and nervous system of the human brain). A neural network replicates the human brain, where the human brain comprises an infinite number of neurons, and coding brain neurons into a system or a machine is what the neural network functions. 

3. Robotics

An interesting field of research and development mainly focuses on designing and constructing robots. Robotics is an interdisciplinary field of science and engineering that incorporates mechanical engineering, electrical engineering, computer science, and many others. Robotics determines the designing, producing, operating, and usage of robots. It deals with computer systems for their control, intelligent outcomes, and information transformation. Robots are deployed often for conducting tasks that might be laborious for humans to perform steadily. 

4. Expert Systems

Expert systems are built to deal with complex problems via reasoning through the bodies of proficiency, expressed especially in particular of “if-then” rules instead of the traditional agenda to code. The key features of expert systems include extremely responsive, reliable, understandable, and high-execution. 

5. Natural Language Processing

NLP is a method that deals in searching, analyzing, understanding and deriving information from the text form of data. In order to teach computers how to extract meaningful information from text data, NLP libraries are used by programmers. A common example of NLP is spam detection, and computer algorithms can check whether an email is junk or not by looking at the subject of a line or text of an email.

Responsibilities of an Artificial Intelligence Engineer

Right now, AI is being integrated into almost every industry vertical, a big reason for the diverse uses of AI. Hence, an AI engineer’s roles and responsibilities have also expanded, which include:

• Software Designing: AI engineers must know machine learning, model building, and validation. They have to analyze and decide whether a particular AI system is capable of serving the purpose it was built for or not.
• Creation and Deployment of AI Algorithms:  Intelligent AI algorithms work with iterative processing to enable software automation. The AI engineer’s responsibilities include writing code essential for the respective machine’s functioning.
• Building Data Science Infrastructure: AI is used for all kinds of data extraction and analytics. That’s why the artificial intelligence engineer sets up and manages the AI infrastructure.
• Data Analytics: Artificial Intelligence engineers also collect data and analyze it using machine learning algorithms that finally identify shortcomings. Artificial intelligence engineers ensure that the AI tools are updated in sync with the breakthroughs. They also continue to deliver operational advantages to the users.
• Natural Language Processing: Prominent artificial intelligence engineer roles include Natural Language Processing with the emergence of smart automation and voice controls. Voice assistants, like Siri and Alexa, depend on an accurate understanding of human speech, and AI engineers make it possible.
• Image Processing: AI is already used in surveillance and monitoring worldwide. These systems can further observe the surroundings and react to each scenario. This is usually done using parameters set by the artificial intelligence engineers who build them. Thus, these professionals are responsible for image processing.

Qualification/Education needed to become an Artificial Intelligence Engineer?

There are several graduate, post-graduate and certification courses in engineering. These programs allow you to specialize in AI, machine learning, and even aid you earn deep learning Certification. These often include engineering degrees in computer science, IT, and data science. Meanwhile, popular degrees and diplomas include 

• B.Tech (Bachelor of Technology) in Artificial Intelligence
• B.Tech in Computer Science Engineering
• B.E. (Bachelor of Engineering) in Artificial Intelligence
• M.Tech (Master of Technology) in Computer Science Engineering

AI is becoming relevant in several industries, such as finance, communication, healthcare, automobile, security, retail, surveillance, and social media. Becoming an AI engineer can open up several opportunities for you in the near future; know about these top 10 AI jobs. AI engineers may also develop their knowledge of applied mathematics, programming languages, algorithmic thought, deep learning, and other neural networks.

Skills Required to Become an Artificial Intelligence Engineer

1. Technical Skills 

Interested professionals must be well-versed in several technical skills that help them know how to learn AI. These include: 

• Programming Skills - You must learn several programming languages to become well-versed in AI. The common ones include Python, R, Java, and C++ which help you build and implement models. 
• Linear Algebra, Probability, and Statistics - You must have detailed knowledge of linear algebra, probability, and statistics to understand and implement different AI models. The common ones include Hidden Markov models, Gaussian mixture models, Naive Bayes, and linear discriminant analysis. 
• Spark and Big Data Technologies - AI engineers work with larger volumes of streaming or real-time production-level data in terabytes or petabytes. Hence, these engineers need to know about Spark and other big data technologies. Along with Apache Spark, they can also use other big data technologies, like Hadoop, Cassandra, and MongoDB.
• Algorithms and Frameworks - Understanding how machine learning algorithms work will help you implement machine learning models with ease. A few common algorithms include linear regression, KNN, Naive Bayes, Support Vector Machine, and others. Moreover, you should understand deep learning algorithms and implement them using a framework to build AI models with unstructured data. It involves working with algorithms like a convolutional neural network, recurrent neural network, and generative adversarial network. Some common frameworks used in artificial intelligence are Theano, PyTorch, TensorFlow, and Caffe.

2. People Skills

Being an AI engineer is a technical job. Yet, the professional must also learn how to communicate with others, deal with problems, and organize one's time effectively. The engineers must work in collaboration with others which helps determine whether the project is successfully completed.

• Communication Skills - Artificial intelligence engineers have to communicate with several people with different levels of competence over time. Suppose a particular company asks you to create and deliver a new AI model to every division inside the company. You should probably brush up on your written and spoken communication abilities if you want to convey complicated thoughts and concepts to a wide audience. 
• Problem-solving Abilities - You must be able to think creatively and solve problems if you want to become a successful artificial intelligence engineer.  Because AI seeks to address problems as they emerge in real-time, it also requires the development of problem-solving skills which can be critical and creative at the same time. 
• Domain Expertise - AI Engineers in the field of machine learning must recognize the demands of the company and the obstacles their designs will address to create self-running programs. This also helps them optimize solutions utilized by organizations and customers. 
• Time Management - Engineers in the field of AI must balance the needs of several stakeholders. This is often associated with the need to conduct research and organize and plan projects. Sometimes, the same people may create software, and thoroughly test it. Hence, the ability to manage one's time is essential to becoming a successful AI engineer or developer.

Careers Available for AI Engineers

Four of the most common occupations in the field of artificial intelligence include:

• Machine learning engineer
• Robotics engineer
• Computer vision engineer
• Data scientist

Besides earning a lucrative package there are more reasons why should you learn AI. Getting on a career path that leads to one of these AI jobs is a smart move if you are planning a longer term in this field. 

For example, a possible career path to becoming an efficient machine learning engineer would begin by doing an entry-level AI job like a software engineer, software programmer, or software developer. You will also need a bachelor’s degree to be eligible for these entry-level jobs.

Your next relevant step is to earn your master’s degree in computer science, data science, software engineering, or similar fields. You may also try gaining some certifications, building your technical skills, and creating a strong portfolio. Finally, get ready to apply for the machine learning engineer position, which should be an upper-level position that pays very well.

Salary of an Artificial Intelligence Engineer

The average salary of an Artificial Intelligence Engineer in India is ₹7,25,000 per year. Meanwhile, the average additional cash compensation for the professional accounts for ₹1,25,000. Its related range will usually be between ₹62,500 to ₹3,82,500.

The salary of an AI engineer in the entry level position is currently ₹4,16,250 per year. An experienced professional, on the other hand, earns ₹11,82,500 per year.

Why become an Artificial Intelligence Engineer?

Artificial intelligence engineering is a rapidly growing and in-demand career field because of the transformative power and benefits of AI. That means it comes with a promising future for the aspiring professionals. As organizations across industries continue to adopt AI technologies, the demand for skilled engineers in this field is expected to increase. 

The best part about being an AI engineer is that these professionals can work in various industries and domains. The common ones include healthcare, finance, manufacturing, and more. This means the professionals get several opportunities for career growth and development as someone experienced in AI tools and techniques. 

Cyber Security Course Program

You must have a relevant bachelor’s degree to become an AI engineer or developer. However, there are certain courses that you can pursue to become more well-versed in this field. The most popular one among them is a cyber security course program. It enables you to explore an extensive range of Cybersecurity essentials, which prove to be beneficial when you work on various projects as an AI engineer. Some of the common essentials include security basics, network security, securing applications, data Security, cryptography, user Security, cyberattacks, user access & identity management.

You can pursue the cyber security certification training course by JanBask training if you are interested in these programs. 

Conclusion

AI engineers work closely with machine learning algorithms and other AI tools to further improve artificial intelligence. They may be required to have good programming and software development skills to become successful at their job. If you are interested in this field, then you must consider developing these early with the help of online resources and forums. JanBask Training understands these requirements and enables you to acquire these skills and knowledge with the help of their extensive range of courses and seasoned trainers. You can get in touch with their experts right now to understand which course works better for you and learn how to become AI engineer with the right skill sets and certifications. 

FAQs

Is Artificial Engineering Hard?

AI engineering can be challenging, especially for professionals who are new to the field. The same applies to those with limited experience in computer science, programming, and mathematics. However, anyone can learn and become proficient in AI engineering with the right training, practice, and dedication.

Can I Become an AI Engineer Without a Degree?

It is possible to become an AI engineer without a degree. But, of course, having a degree in a related field can be helpful. Many AI engineers have backgrounds in mathematics, computer science, or statistics, but there are also several online courses, boot camps, and other training programs that offer practical experience in several aspects of AI development. Hence, you should be willing to learn and stay up-to-date with the latest AI developments.

How Long Does it Take to Become an AI Engineer?

The time taken to become an AI engineer depends on several factors. Examples include a person’s current level of knowledge, experience, and the learning path chosen by the professional. However, it may take around 6-12 months to gain the necessary skills and knowledge to become an expert AI engineer.