The IT roles needed in the AI era go beyond AI Engineers. When companies integrate AI into their existing applications, data, and infrastructure, talent needs usually spread across six roles: AI/ML Engineer, Software Developer with AI skills, Data Engineer, Cloud and DevOps Engineer, QA and Automation Engineer, and Cybersecurity Engineer. Which role takes priority depends on what you want to build.

The shift is already visible in the job market. PwC's 2026 Global AI Jobs Barometer, which analyzed more than one billion job postings across 27 countries and territories, found that job postings requiring specific AI skills grew by around 69%, while the overall job market grew by 9%. The number of AI jobs has also nearly doubled compared to 2024.

PwC also noted that skills in the jobs most exposed to AI are changing more than twice as fast as in the jobs least exposed to AI. This means the challenge for companies is not only finding new roles, but making sure existing roles have the right combination of skills.

This article covers those six roles and how to define the talent requirements for your project.

1. AI / Machine Learning Engineer

The AI Engineer is the most obvious role when discussing AI growth. This role focuses on developing, implementing, and integrating AI or machine learning systems, from model development and use of existing AI models, to evaluation and connecting AI with applications or business processes.

The growth shows up in LinkedIn data. In Jobs on the Rise 2026 for the United States, AI Engineer ranked first among the 25 fastest-growing roles over the past three years. Note that this data is specific to the US market and does not necessarily reflect conditions in Indonesia.

Not every company that wants to use AI automatically needs a large AI Engineer team. The role you need still depends on what you want to build. Developing a proprietary AI model has different requirements from integrating existing AI capabilities into internal applications.

2. Software Developer with AI Skills

This is one of the changes we think deserves attention. AI does not remove the need for software developers, but the way developers work is starting to change.

Developers can now use AI-assisted tools for code generation, debugging, documentation, code explanation, test generation, refactoring, and exploring solutions. Because of this, it is no longer enough for companies to check only whether a developer masters a particular programming language or technology stack.

The ability to use AI productively, evaluate its output, understand security implications, and keep code maintainable is becoming a relevant additional competency.

In other words, an AI-skilled developer does not have to be an AI Engineer. Developers can stay focused on Java, .NET, frontend, mobile, or other technology stacks, while the way they work is increasingly shaped by AI.

3. Data Engineer

The more companies want to use AI, the more important the questions about data become. Models and AI applications need data that is accessible, processed, integrated, and of adequate quality.

This is where the Data Engineer comes in. They build and maintain data pipelines, integrate multiple data sources, and make sure data can be used by both analytics and AI systems.

Without an adequate data foundation, a company may have AI tools but struggle to produce output relevant to its business context. AI investment does not only create demand for people who build models. It also raises the importance of talent who ensure the data behind AI is consistent and reliable.

4. Cloud and DevOps Engineer

AI applications still need infrastructure. When an AI proof-of-concept starts moving into production, companies need to consider deployment, scalability, performance, monitoring, availability, and cost.

This role helps ensure that applications, including those using AI, can:

1. be deployed consistently;

2. run on suitable infrastructure;

3. be monitored after going into production;

4. scale as usage grows;

5. be integrated with the development pipeline.

AI may change how software is built, but the need to run software reliably does not go away. The more complex the technology environment, the more important the infrastructure and delivery foundation behind it becomes.

5. QA and Automation Engineer

AI can help developers produce code faster. However, more code does not automatically mean better software. Output still needs to be validated.

QA and Automation Engineers make sure that changes, whether written by humans or assisted by AI, still meet functional requirements, performance expectations, and quality standards. AI can also be used in QA workflows, for example to help generate test scenarios, but using it does not remove the need for quality judgment.

As development gets faster, testing and validation capabilities need to keep pace with the increase in output.

6. Cybersecurity Engineer

AI opens new productivity opportunities while also expanding the area that needs to be secured. AI-powered applications raise new questions about data access, authentication, model interaction, third-party services, and how sensitive information is used.

At the same time, developers who use AI-generated code still need to make sure the output meets the company's security standards.

For that reason, cybersecurity is not a function separate from AI transformation. The more widely AI is used in the technology environment, the more important it is for organizations to keep its adoption within appropriate governance and security controls.

Read Also: Why Companies Are Starting to Look for AI Engineering Skills, Not Just Programming Skills

AI Engineer Is Not the Only Answer

There is a tendency to view talent needs in the AI era through a single new role: the AI Engineer. In reality, company needs are far more contextual.

1. If the company wants to build machine learning capability, the AI/ML Engineer may be the main role.

2. If AI will be integrated into an existing application, the company may need a Software Developer who understands AI integration more.

3. If the problem lies in the data foundation, the Data Engineer may be the priority.

4. If the AI application is heading to production, the need for Cloud, DevOps, QA, and Cybersecurity becomes relevant too.

In other words: do not start from the role name. Start from what you want to build.

AI Changes the Skills Within Roles, Not Just Create New Roles

This may be the more important shift for workforce planning. Existing roles also need to evolve:

1. Software Developers need to understand how to use and evaluate AI-assisted development.

2. QA needs to understand how AI supports testing, and also how AI-enabled features are validated.

3. Data Engineers are moving closer to the data needs of AI.

4. Cloud and DevOps need to support new workloads.

5. Cybersecurity needs to deal with an expanding attack surface and governance requirements.

For this reason, workforce strategy in the AI era should not stop at the question "How many AI Engineers should we hire?" A more useful question is: "Which roles in our project need AI-related skills, and how deep do those skills need to be?"

Find out how IT Professional Services can help you meet your need for AI-skilled programmers.

From AI Specialists to AI-Skilled IT Professionals

Not all IT talent has to become an AI specialist. Companies still need developers, analysts, QA, data professionals, infrastructure specialists, and many other technical roles. What changes is the combination of skills within those roles.

A Software Developer with strong programming fundamentals and the ability to use AI-assisted tools offers different value from a developer who relies only on AI to generate code. The same goes for a Data Engineer who understands AI workload requirements, a QA Engineer who can adapt testing for AI-enabled applications, or a DevOps Engineer who understands deploying AI workloads.

AI skills are an additional layer of competency, not a replacement for technical foundations.

What Does This Mean for IT Professional Services?

The shift in role requirements means companies need to be more specific when looking for talent for a project.

A request like "we need a developer" may no longer be enough.

Companies need to consider:

1. the technology stack required;

2. the type of application or system to be developed;

3. whether the project involves AI;

4. how AI will be used;

5. the integration experience required;

6. data and infrastructure needs;

7. and the level of AI skill relevant to the role.

Through IT Professional Services, Indocyber helps companies meet their needs for professional IT talent and technical expertise based on project requirements.

As industry needs evolve, talent searches can also include programmers and IT professionals whose skills are increasingly relevant to AI-enabled workflows, without changing the primary focus on matching technical expertise to project needs.

Because in the end, companies do not need the role that is most hyped. They need the right combination of roles and skills for what they want to build.

Looking for IT Talent for Your Project in the AI Era?

Start from your project needs and technology stack, all the way to the competencies required for each role.

Discuss your IT Professional Services needs and IT talent requirements with our professional team.

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