Careers in AI: salaries, transitions, and pathways for developers

AI engineers’ salaries surpass those of traditional developers

According to Robert Half’s 2026 Salary Guide, an artificial intelligence engineer can start with a monthly salary of R$19,500, while a full-time backend developer earns between R$9,500 and R$15,900. The national salary projection for AI engineers in 2026 ranges from R$19,500 to R$27,100, with the minimum corresponding to the 25th percentile, typical for professionals starting out in this role.

For comparison, full-stack developers have a salary range of R$9,550 to R$15,900. The difference of R$3,600 between the projected minimum salary for AI and the maximum salary for a full-stack developer represents approximately 23% more than the maximum salary in a development career.

These figures are market references. Actual compensation depends on experience, responsibilities, company, and required skills.

The daily routine of an AI engineer differs from that of a conventional developer

While traditional developers create and maintain applications, APIs, and databases, AI engineers also implement intelligent features, integrate models, and evaluate the results generated by these systems.

In practice, they can build an assistant that queries internal documents, automate information analysis, or create a system that classifies customer requests. To achieve this, it is necessary to combine programming with specific AI knowledge.

Microsoft’s technical documentation lists the skills required for this role as developing, deploying, integrating, maintaining, and monitoring AI solutions. Python, APIs, and SDKs are relevant technologies for this work.

Previous experience in development can facilitate the transition. Knowledge of software architecture, testing, security, databases, and deployment remains useful, but it is essential to learn how AI models behave and how to evaluate their responses.

Time required for a career change

There is no single timeframe for transitioning from software development to AI engineering. The duration depends on the professional’s technical background, the desired specialization, and the dedication to studies and projects.

An estimate published by the website HowLongFor in August 2026 suggests that the transition for professionals already working in development can take between six months and two years. This timeframe serves as a planning reference, not as a guarantee of employment.

For those who already know how to program, the journey can begin with mastering the Python language, followed by using language models, API integration, information retrieval, and result evaluation.

Education can combine free courses, certifications, undergraduate, or postgraduate degrees. Roles focused on building applications with existing models have different requirements than those involving research and development of new models.

How to develop the skills required by the market

An effective strategy is to use current experience to build projects with AI features. For example, a backend developer can create an API that queries documents and uses a language model to answer questions based on the retrieved content.

This type of application can involve retrieval-augmented generation (RAG), a technique that combines generative models with information retrieval from external sources. It also requires testing to identify incorrect answers, access control, and performance monitoring.

Microsoft’s documentation includes Python, application development, and implementation of generative solutions among the skills related to AI engineering.

More than accumulating certificates, professionals need to demonstrate that they can transform this knowledge into functional systems. Published projects, technical documentation, and verifiable results help demonstrate this ability during recruitment processes.

Real-life experience: from data analyst to engineer at StackAI

The career path of Jai Raj Choudhary, reported by Business Insider in February 2026, illustrates a possible transition. He worked in a data-related role before becoming an AI engineer at the startup StackAI.

With a master’s degree in artificial intelligence, Choudhary reached out to the company’s co-founder on LinkedIn and provided feedback on the platform. After six rounds of interviews, he secured the position. In the report, he emphasized the importance of understanding data systems and grasping customers’ concrete problems.

This case does not represent a universal formula, but it shows that a career change can combine specialized education, technical experience, and initiative to demonstrate skills directly to employers.

Is it worth transitioning to artificial intelligence?

The salary difference is an incentive, but it should be evaluated alongside job requirements and the investment needed to acquire new skills. An experienced developer can leverage a significant portion of their existing education without having to start their career from scratch.

Before making a decision, it is worth comparing the responsibilities of the roles, identifying missing skills, and testing the field with practical projects. Higher pay is a career possibility, not a result guaranteed by specialization.