How to Become an AI Engineer: Skills, & Salary [2025]

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:

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 (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.

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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.

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.

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.

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

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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.