Could Moving Into Artificial Intelligence Lead to a Higher Salary?

The rapid expansion of artificial intelligence is changing the technology job market and encouraging many professionals to reconsider their career paths. For software developers, moving into AI-related roles could potentially open the door to higher salaries, although the transition requires more than simply learning how to use popular AI tools.

According to the 2026 Salary Guide from Robert Half, AI engineers in Brazil are projected to earn between R$19,500 and R$27,100 per month. By comparison, the projected salary range for mid-level backend developers is between R$9,500 and R$15,900, while mid-level full-stack developers are expected to earn between R$9,550 and R$15,900.

At the lower end of the AI engineering range, the projected R$19,500 figure is already R$3,600 higher than the upper end of the salary range for mid-level backend developers. That difference can make AI specialization attractive to technology professionals looking for new career opportunities.

However, these figures should not be interpreted as an automatic pay increase. Actual compensation depends on experience, responsibilities, company size, technical expertise and the specific position.

What changes when a developer moves into AI?

A traditional software developer may spend much of their time building applications, APIs, databases and software infrastructure. An AI engineer adds another layer to that work by integrating intelligent models into applications and creating systems capable of processing information and generating useful outputs.

For example, an AI-focused developer could build an internal assistant capable of searching company documents, automate the classification of customer requests or create a system that uses a language model to help employees find information.

This means programming remains important, but professionals also need to understand how AI models work, how to connect them to applications and how to evaluate whether their results are reliable.

Skills such as Python, APIs, software architecture, databases, security, testing and deployment can provide a strong foundation for this transition. Microsoft documentation also highlights development, deployment, integration, maintenance and monitoring as important aspects of AI engineering.

How long does the transition take?

There is no universal timeline for moving from conventional software development into AI engineering. Someone with several years of programming experience may have a significant advantage over someone starting from scratch.

The amount of time required will depend on the professional's existing skills, the type of AI role they want and how much time they can dedicate to learning and practical projects.

For developers, a possible starting point is Python, followed by concepts such as generative AI, language models, APIs, information retrieval and AI system evaluation.

Not every AI career requires the same level of specialization. Building applications around existing models is different from developing or researching new machine-learning models, so professionals should choose their learning path according to the type of position they want.

Practical projects can be more valuable than simply collecting certificates

One effective way to demonstrate AI skills is by building real projects.

A backend developer, for instance, could create an application that searches a collection of documents and uses a language model to answer questions based on the information retrieved. This type of project can involve retrieval-augmented generation, commonly known as RAG.

It can also require authentication, access controls, testing, monitoring and methods for identifying inaccurate AI responses.

This kind of practical experience demonstrates that a professional can do more than operate an AI chatbot. It shows an ability to incorporate AI into functioning software systems.

That distinction is becoming increasingly important as companies look for people capable of turning artificial intelligence into useful business applications.

AI skills are becoming valuable beyond a single job title

The growing demand for AI professionals does not mean that every technology worker needs to become a machine-learning researcher.

AI can be combined with existing areas of expertise. A software developer can add AI capabilities to applications. A data professional can work with machine-learning systems and automation. A cloud specialist can focus on deploying and operating AI workloads.

The 2026 AI Jobs Barometer from PwC also points to broader changes in the labor market, with AI increasingly affecting skills, salaries and the way companies create value.

This suggests that professionals may benefit from combining AI knowledge with an established technical or business skill instead of abandoning their previous experience completely.

Is moving into AI worth it?

For technology professionals considering a career change, artificial intelligence can represent an attractive opportunity. Salary projections show that specialized AI positions can command significantly higher compensation than some traditional mid-level development roles in Brazil.

But salary should not be the only factor behind the decision.

The transition requires continuous learning, practical experience and an understanding of how AI systems can be integrated into real products and business processes. Professionals who already have strong programming or technical backgrounds may be able to build on those skills rather than starting their careers over.

Ultimately, the strongest combination may be technical experience plus AI expertise. Learning how to build, integrate and evaluate AI systems can help technology professionals remain competitive as the industry continues to evolve.