How to Become an AI Engineer

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 about how to become an AI engineer.

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What Is Artificial Intelligence Engineering?

Artificial Intelligence Engineering is a branch of engineering focused on designing, developing, and managing systems that integrate artificial intelligence (AI) technologies. This discipline encompasses the methods, tools, and frameworks necessary to implement AI solutions effectively within various industries.

AI engineers work on creating algorithms, building advanced data processing techniques, and improving the robustness and performance of AI systems, ensuring they can solve complex problems, automate processes, and optimize operations efficiently. Their role is critical in bridging the gap between theoretical AI developments and practical, real-world applications, ensuring AI systems are scalable, sustainable, and ethically aligned with societal norms and business needs.

Also Read: Top Artificial Intelligence Applications

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.  AI 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 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.

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How to Become an Artificial Intelligence Engineer?

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

Step 1. 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.

Step 2. 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).

Step 3. 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.

Step 4. 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.

Step 5. Acquire Practical Experience

To become an AI engineer, it's important to acquire practical experience. While a strong foundation in mathematics, statistics, and computer science is essential, hands-on experience with real-world problems is equally important. Through projects, and participation in hackathons, you can develop practical skills and gain experience with a variety of tools and technologies used in the field of AI engineering. Additionally, online courses and bootcamps can provide structured learning and mentorship, allowing you to work on real-world projects and receive feedback from industry professionals. With a combination of theoretical knowledge and practical experience, you can become a skilled AI engineer and contribute to the growing field of artificial intelligence.

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AI Engineering Examples in Real World

Artificial Intelligence (AI) engineering is implemented across various sectors, transforming traditional operations and enabling new capabilities. Here are some real-world examples of AI engineering applications:

  1. Healthcare: AI engineering is leveraged in developing predictive analytics tools for patient care, automated diagnosis systems, and personalized medicine. AI algorithms analyze medical images, predict disease progression, and recommend treatment plans tailored to individual patients.
  2. Automotive: AI engineers develop autonomous driving systems in the automotive industry. These systems use machine learning algorithms to interpret sensor data, allowing vehicles to make real-time decisions about navigation, obstacle avoidance, and safety protocols.
  3. Finance: AI is used in algorithmic trading, where engineers design systems that can analyze large volumes of financial data to make automated trading decisions at speeds and volumes impossible for human traders. AI is also applied in fraud detection, using pattern recognition to identify unusual transactions quickly and accurately.
  4. Retail: In retail, AI engineering contributes to personalized shopping experiences by analyzing customer data to predict purchasing behaviors and recommend products. Inventory management systems also use AI to forecast demand and optimize stock levels.
  5. Manufacturing: AI-driven predictive maintenance systems are engineered to monitor equipment conditions through sensors. These systems predict when machines will likely need maintenance, preventing costly downtime and extending the machinery’s life.
  6. Smart Cities: AI engineering plays a vital role in smart city initiatives by optimizing traffic management systems, enhancing public safety through surveillance systems with real-time processing, and managing energy distribution more efficiently.
  7. Agriculture: AI technologies help in precision farming, where engineers develop systems to monitor crop health, predict yields, and optimize resource use (water, fertilizers) based on data collected via sensors and drones.

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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

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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.

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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.

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, 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.

"In the future, AI will be able to do anything that a human mind can." - Ray Kurzweil

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.

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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.

AI Engineer Salary

According to Glassdoor, the average annual salary of an AI engineer is $130K in the United States and ₹11 Lakhs in India. The salary may differ in several organizations, and with the knowledge and expertise you bring to the table.

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How to Build a Career in AI?

In addition to learning how to become an AI engineer, it is essential to understand which industry you want to work with. Since several sectors 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:

AI Developer 

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

AI Architect 

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

Machine Learning Engineer 

Machine learning 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.

Data Scientists 

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

Business Intelligence Developer 

They're responsible for designing, modeling, and analyzing complex data to identify business and market trends.

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Conclusion

If you wish to unlock the power of Artificial Intelligence and lead the future, you must enroll in our AI Engineer program. Learn ML, NLP, deep learning, over a dozen of top sills and tools and so much more. Get started!

FAQs

1. Is AI engineering a good career?

Yes, AI engineering is a rapidly growing and in-demand career field with a promising future. As organizations continue to adopt AI technologies, the demand for skilled AI engineers is only expected to increase. AI engineers can work in various industries and domains, such as healthcare, finance, manufacturing, and more, with opportunities for career growth and development.

2. Is AI engineering hard?

AI engineering can be challenging, especially for those who are new to the field and have limited experience in computer science, programming, and mathematics. However, with the right training, practice, and dedication, anyone can learn and become proficient in AI engineering. It requires a strong foundation in computer science, knowledge of machine learning algorithms, proficiency in programming languages like Python, and experience in data management and analysis.

3. Can You Become an AI Engineer Without a Degree?

While having a degree in a related field can be helpful, it is possible to become an AI engineer without a degree. Many successful AI engineers have backgrounds in computer science, mathematics, or statistics, but there are also a growing number of online courses, bootcamps, and other training programs that offer practical experience in AI development. It is important to have a solid foundation in programming, data structures, and algorithms, and to be willing to continually learn and stay up-to-date with the latest developments in the field.

4. How long does it take to become an AI engineer?

The time it takes to become an AI engineer depends on several factors such as your current level of knowledge, experience, and the learning path you choose. However, on average, it may take around 6 to 12 months to gain the necessary skills and knowledge to become an AI engineer. This can vary depending on the intensity of the learning program and the amount of time you devote to it.

5. How can I become an AI after 12th?

To pursue a career in AI after 12th, you can opt for a bachelor's degree in fields like computer science, data science, or AI. Focus on learning programming, mathematics, and machine learning concepts. Further, consider pursuing higher education or certifications to specialize in AI.

6. What is the AI engineer salary in India?

The salary of an AI engineer in India can vary based on factors such as experience, location, and organization. On average, entry-level AI engineers can expect a salary ranging from INR 6 to 10 lakhs per annum. With experience and expertise, the salary can go up to several lakhs or even higher, depending on the individual's skills and the company's policies.

7. Are AI engineers well paid?

Yes, AI engineers are typically well-paid due to the high demand for their specialized skills and expertise in artificial intelligence and machine learning. Their salaries can vary based on experience, location, and the specific industry they work in, but generally, they command competitive compensation packages.

About the Author

Mayank BanoulaMayank Banoula

Mayank is a Research Analyst at Simplilearn. He is proficient in Machine learning and Artificial intelligence with python.

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