For many years, the evolution of technology teams moved towards specialization. There was a professional to write code, another to define requirements, one responsible for infrastructure, another for operations, and above them, a CTO concentrating the company's main technical decisions.

This model continues to work in many organizations, especially in large ones. But the advancement of artificial intelligence, cloud computing, and development platforms is accelerating an important change: professionals who previously only worked on implementation have started to participate directly in product, architecture, and business decisions.

It is in this context that the Product Engineer gains ground. The CTO is not going to disappear, nor has DevOps stopped being important. What is changing is the distribution of responsibilities within teams. Part of the decisions, which previously depended exclusively on technical leadership, can now be made by engineers with strong product knowledge, supported by increasingly intelligent tools and a widely automated infrastructure.

Specialization and product context

For decades, software engineering was organized in well-defined layers:

The Product Manager understood the customer's problem.

The UX Designer designed the experience.

The developer implemented the solution.

The DevOps took care of the infrastructure.

The CTO defined architecture, standards, and technological strategy.

This division made sense when each layer required very specific knowledge, and communication between areas represented a lower cost than distributing responsibilities.

Today this scenario has changed. AI tools can generate prototypes, review code, suggest architectures, and automate repetitive tasks. At the same time, cloud platforms abstract away much of the operational complexity that previously required dedicated teams just to keep applications running.

As a consequence, the expectation is growing for engineers to understand not only how to build, but why to build.

According to an article published by CIO, AI-driven companies have been observing significant gains by bringing engineering and product closer together, reducing the number of intermediary decisions and increasing team autonomy. In the text, the CTO of the enterprise AI platform akirolabs describes this evolution as the transition from a specialist engineer to a professional deeply connected to the business context. The reported experience showed reductions of between 15% and 25% in development time after adopting this model, reaching cycles up to 45% faster when combined with modern AI tools.

Although these numbers are internal results of the company (and not a scientific study), they illustrate a trend observed in much of the market: speed no longer depends only on code quality and has started to depend on the quality of decisions.

What defines a Product Engineer?

Despite the growing popularity of the term, there is still no single definition for a Product Engineer. Companies like Vercel, Linear, Notion, and various Silicon Valley startups use similar concepts to describe professionals who combine three traditionally separate skills:

• software development;

• product vision;

• ability to make technical decisions autonomously.

In practice, the Product Engineer remains a software engineer. The difference lies in the scope of action.

Instead of simply receiving tasks, this professional participates in discussions that define priorities, understands business metrics, evaluates user impact, and frequently influences architectural decisions that previously would have depended exclusively on more senior professionals.

This completely changes the team dynamic. By understanding the business problem, the engineer stops being just an executor and becomes co-responsible for the product's outcome.

This autonomy also reduces one of the biggest bottlenecks in modern development: the excess of handoffs between areas. The less a decision needs to travel across different teams, the shorter the time between idea and delivery tends to be.

Is AI to blame?

It is common to associate the emergence of the Product Engineer with the advancement of artificial intelligence. In reality, AI did not create this movement. It accelerated something that was already happening.

Generative models began to perform activities that consumed a significant part of the engineering routine:

• code generation;

• documentation;

• automated testing;

• refactoring;

• log analysis;

• explaining existing architectures;

• prototyping.

With less time spent on operational tasks, the space for decisions that require an understanding of the product increases.

At the same time, tools like GitHub Copilot, Claude Code, Cursor, and other development assistants have reduced the cognitive cost of implementation, allowing engineers to concentrate energy on solving the problem, rather than just writing code.

This also changes the role of technical leadership. The CTO spends less time resolving operational questions and more on defining technological strategy, governance, security, architecture, and organizational direction.

If the Product Engineer grows, does DevOps lose ground?

What is changing is not the importance of DevOps culture, but the need to maintain a dedicated specialist to perform tasks that can now be automated by modern platforms.

A few years ago, creating an infrastructure required manually configuring servers, installing services, preparing deploy pipelines, and managing practically the entire operation of the application. Today, much of this work can be abstracted by tools like Terraform, GitHub Actions, managed Kubernetes, and the various services offered by providers like AWS, Azure, and Google Cloud.

In addition, a concept that has been treated as the natural evolution of DevOps is gaining strength: Platform Engineering.

Instead of each squad constantly depending on infrastructure specialists, platform teams build standardized, reusable, and secure environments, allowing developers to work with much more autonomy without giving up governance.

In this scenario, the Product Engineer does not replace DevOps. They start to consume a ready-made platform. It is a shift similar to what happened with managed databases. Few companies stopped using databases, they just stopped manually managing all the infrastructure needed to keep them running. The same logic is starting to appear in software engineering as a whole.

What do companies gain from this new profile?

The rise of the Product Engineer addresses a concrete challenge: how to increase speed without proportionally increasing the size of the teams.

When an engineer understands the product context and has the autonomy to navigate through different technical layers, some gains naturally tend to appear:

• fewer handoffs between teams;

• reduction of communication bottlenecks;

• shorter cycles between idea and delivery;

• greater sense of ownership over the final result;

• less dependence on specialists for routine tasks.

Infrastructure areas, in turn, begin to act more strategically, developing reusable platforms instead of fulfilling specific demands of each project. In the end, the entire organization gains the ability to scale.

A new kind of infrastructure for a new kind of team

The autonomy that these changes bring does not depend only on more versatile professionals. It also requires an infrastructure capable of eliminating friction between idea and delivery.

This is why the number of platforms developed to abstract part of the operational complexity of development is growing, offering standardized environments, automated pipelines, and engineering best practices right from the start of the project.

An example is CodeCell. Besides outsourcing top-tier professionals, this service provides a state-of-the-art technological foundation for new digital products, allowing teams to focus their efforts on building features, considering aspects like cloud architecture, infrastructure, and automation. The proposal is not to replace infrastructure specialists, but to allow them to act more strategically while Product Engineers work with more autonomy on a consistent technical foundation.

What lies ahead

The growth of the Product Engineer shows that the evolution of software engineering is less about creating new job titles and more about the redistribution of responsibilities.

As platforms, artificial intelligence, and automation take over part of the operational complexity, technology professionals gain space to participate in decisions that connect code, product, and business.

The challenge for companies ceases to be just hiring specialists and becomes building environments that allow these professionals to have the autonomy to deliver more, safely and with context.