Careers in artificial intelligence: skills, salaries, and how to enter the Brazilian market

Professionals looking to work in artificial intelligence will find a field that goes beyond simply using generative tools. The highest-paying positions require the ability to develop solutions, manipulate large volumes of data, and integrate technologies into business processes.

According to Robert Half’s 2026 Salary Guide, an artificial intelligence engineer in Brazil can earn between R$19,500 and R$27,100 per month. The range varies depending on experience and qualifications and does not represent a guaranteed salary for those entering the field.

To secure these roles, it’s helpful to understand what skills are required and how to apply them in practice.

Programming is the starting point

Python is one of the most widely used languages in artificial intelligence and data science projects. Learning to program enables you to manipulate information, automate tasks, and create applications that rely on AI models.

In addition to the language itself, it’s important to study data structures, algorithms, and databases. These fundamentals help you understand how applications work and solve problems that go beyond using off-the-shelf tools.

Mathematics, statistics, and machine learning

Anyone looking to develop AI models needs to master concepts in probability, statistics, linear algebra, and optimization. This knowledge allows you to interpret data and evaluate algorithm performance.

Machine learning involves techniques for identifying patterns in data and generating predictions or classifications. Practical projects are essential for understanding how to prepare data, train models, and measure result quality.

Data engineering and AI applications

Models rely on organized and reliable data. Therefore, studying data engineering, information processing, and system integration can expand career opportunities.

Another area is generative AI, which can produce text, images, code, and other content. To work with these applications, it’s helpful to understand how language models operate, how to connect them to information sources, and how to evaluate their responses.

Knowledge of APIs, which enable communication between programs, and deployment tools helps transform experiments into solutions that businesses can use.

Portfolio and experience matter in the selection process

Courses provide a theoretical foundation, but projects are the most effective way to demonstrate what a professional can do. A good idea is to develop an application that organizes documents, automates a task, or analyzes a dataset, documenting the decisions made and the results achieved.

Publishing the code, explaining the challenges faced, and documenting tests allows recruiters to assess technical skills. For those already working in another field, projects related to their own sector can help connect professional experience with technology.

Technical knowledge isn’t the only requirement

Communication, analytical thinking, and problem-solving skills are also important. Professionals need to explain limitations, assess risks, and understand the needs of those who will use the solutions.

For those just starting out, the most consistent path is to build a technical foundation, choose a specialization, and develop progressively more complex projects.