The 5 professional profiles that AI will demand by 2027
Artificial intelligence is entering a distinct stage of business adoption. The focus is no longer solely on experimenting with generative models or incorporating assistants into specific tasks. Organizations are beginning to integrate AI systems into products, operations, data analysis, customer service, software development, and decision-making processes that require greater technical control.
By 2025, 20% of companies in the European Union with at least ten employees were already using some form of artificial intelligence technology, up from 13.5% recorded in 2024. Among large European companies, adoption reached 55%. This expansion creates professional needs that encompass development, integration, evaluation, security, and governance of systems.
If you want to stay informed about tech talent management, hiring and new trends, subscribe.
The Future of Jobs Report 2025 from the World Economic Forum places artificial intelligence specialists and machine learning among the fastest-growing tech professions by 2030. At the same time, it identifies AI and big data as the competencies with the highest expected growth. Looking ahead to 2027, this scenario allows us to distinguish five profiles particularly relevant for organizations that are moving AI from experimental projects to productive environments.
1. AI engineer: the profile that brings artificial intelligence to production
The AI engineer develops and integrates artificial intelligence solutions within business applications and processes. Their work combines programming, AI models, APIs, data, and infrastructure to convert an algorithmic capability into a solution that can be used stably within an organization.
Their professional scope has expanded with generative AI. In addition to working with predictive models or traditional machine learning systems, they may participate in applications based on large language models, RAG systems, AI agents, document processing, and intelligent automation. The ability to integrate these solutions with corporate applications and data is especially relevant.
A professional of this type needs solid programming knowledge—usually Python—APIs, databases, model evaluation, and machine learning fundamentals. When solutions move to production, cloud, security, monitoring, cost control, and scalability also become important. Companies developing these capabilities can strengthen their teams with artificial intelligence specialists prepared to work on business projects.
2. Generative AI & LLM engineer: specialization in generative models
The expansion of large language models has created a specialization aimed at building products and functionalities on generative models. The generative AI or LLM engineer works with a technological layer that includes model selection, information retrieval, embeddings, vector databases, agents, evaluation, and security mechanisms.
One of their responsibilities is to connect generalist models with the specific knowledge of an organization. In a RAG system, for example, the model can consult corporate documentation before generating a response. This approach allows for the development of internal assistants, semantic search engines, support systems, tools for analyzing documents, or intelligent functionalities integrated into digital products.
The profile requires understanding both the capabilities and limitations of the models. Response quality, latency, cost per inference, privacy, hallucinations, security, and evaluation are part of the technical decisions. In more advanced architectures, this specialization is closely related to machine learning engineers and professionals capable of preparing the necessary infrastructure to operate models reliably.
3. Prompt engineer and AI interaction specialist: a profile in transformation
Prompt engineer quickly became one of the most recognized terms in the early stage of generative AI. Their role involves designing instructions, contexts, and interaction strategies that allow for more consistent results from the models. However, looking towards 2027, it is important to understand this specialization within a broader professional scope.
Professionally working with prompts involves analyzing how a model responds to different instructions, building test sets, evaluating results, controlling output formats, and designing mechanisms to reduce incorrect responses. In business applications, they may also be involved in defining the context that the model receives and how it uses tools, documentary sources, or information from other systems.
Technological evolution is causing these competencies to be progressively integrated into AI engineering, product, automation, and interaction design functions. Therefore, mastering prompt engineering can constitute a valuable skill within a more comprehensive technical profile, especially when combined with programming, model evaluation, and domain knowledge where the solution is implemented.
4. AI ethics & governance specialist: governing the business use of AI
As artificial intelligence intervenes in processes impacting people, customers, or employees, organizations need to establish clear criteria for its use. The AI ethics & governance specialist works on policies, risks, responsibilities, transparency, human oversight, and control mechanisms associated with the lifecycle of AI systems.
Their activities may include creating internal frameworks to approve use cases, risk classification, model documentation, bias analysis, and defining responsibilities among technology, business, security, legal, and compliance. The role requires a sufficient understanding of technology to assess its implications while interpreting regulatory and organizational requirements.
This profile is particularly important in Europe. The AI Act already applies certain provisions regarding AI literacy, governance, and general-purpose models, while rules regarding certain high-risk systems will begin to apply on December 2, 2027. Regulation thus contributes to making AI governance an operational discipline that requires specialized knowledge.
5. AI auditor: evaluating models, risks, and controls
The AI auditor represents a specialization that is gaining relevance as organizations need to demonstrate how their artificial intelligence systems function and how they are controlled. Their work involves evaluating processes, documentation, controls, risks, and evidence related to the development and use of these systems.
An audit can analyze the origin and quality of certain data, technical documentation, oversight mechanisms, traceability, model behavior, access controls, or processes used to detect deviations. The scope will depend on the type of system, the sector, and its level of risk.
This role requires an unusual combination of technical, analytical, and regulatory capabilities. An auditor needs to understand concepts related to models and data to interpret technical evidence, but they must also be familiar with risk, governance, and compliance methodologies. In particularly sensitive projects, their work may relate to cybersecurity specialists, privacy, legal, and technology risk management.
How the five AI profiles differ
Although all work around artificial intelligence, they intervene at different moments in the lifecycle. Engineering profiles focus their activity on construction and integration, while governance and auditing specializations gain greater weight when systems must operate under organizational and regulatory controls.
| Profile | Main function | Key competencies | Scope of action |
|---|---|---|---|
| AI Engineer | Develop and integrate AI solutions | Python, APIs, models, data, cloud | Development and production |
| Generative AI / LLM Engineer | Build applications on generative models | LLM, RAG, embeddings, evaluation, agents | Generative AI |
| Prompt Engineer / AI Interaction Specialist | Optimize interaction with models | Prompting, evaluation, context, testing | Experience and behavior of the model |
| AI Ethics & Governance Specialist | Define policies and controls | Responsible AI, risks, regulation, governance | AI governance |
| AI Auditor | Evaluate systems and evidence | Auditing, models, data, controls, compliance | Evaluation and assurance |
The difference between these professionals shows the maturation of the market. Implementing AI in an organization requires different capabilities during design, development, deployment, monitoring, and evaluation. As the criticality of use cases increases, so does the specialization of those involved.
The competencies that will gain value alongside AI
The growth of these profiles is accompanied by a transformation of technological competencies. The World Economic Forum estimates that 39% of current worker skills will change or become outdated between 2025 and 2030. AI and big data lead the competencies with the highest expected growth, followed by networks and cybersecurity and by technological literacy.
This evolution favors professionals capable of combining knowledge from various disciplines. An AI engineer needs to understand software and data; a governance specialist must interpret technology and risk; an AI auditor needs to evaluate controls without losing sight of the technical behavior of the system.
Human capabilities also remain important. Analytical thinking, creativity, communication, continuous learning, and collaboration appear among the competencies that employers consider relevant for the coming years. Technological sophistication increases the value of professionals capable of explaining complex decisions and working with multidisciplinary teams.
What profiles a company needs according to its maturity in AI
An organization that is beginning to experiment with artificial intelligence has different needs from one that is already operating AI systems on critical processes. During an initial phase, it is usually a priority to validate use cases and have professionals capable of building technically viable prototypes. When solutions generate value, new demands related to integration, data, infrastructure, and maintenance arise.
The transition to production increases the importance of engineering, observability, security, and evaluation. Subsequent growth forces the establishment of standards that allow for the reuse of components, cost control, and maintaining consistent behavior across different solutions. Data engineers can also play a relevant role when the quality and availability of information condition the functioning of systems.
In organizations with numerous use cases or regulated sectors, governance and auditing gain greater prominence. The final composition depends on the product, the data used, the impact of automated decisions, applicable regulation, and the existing technological infrastructure.
Business adoption is expanding the AI talent market
European data shows that the adoption of artificial intelligence is accelerating. The percentage of EU companies using these technologies rose from 13.5% in 2024 to 20% in 2025. Among medium-sized companies, it reached 30.4%, and among large companies, 55%.
Growth is also diversifying uses. European companies use AI to analyze written language, generate text, voice, and images, recognize information, analyze data through machine learning, and automate workflows. Each new application introduces needs related to development, integration, data, monitoring, and control.
The labor market reflects this trend. The World Economic Forum ranks AI and machine learning specialists among the fastest-growing positions by percentage towards 2030 and notes that 86% of surveyed employers expect AI and information processing technologies to transform their businesses during that period.
Hiring AI talent in 2027 will require better defining each profile
Simply searching for an “artificial intelligence expert” will become less precise. Two professionals may work within the same field and have very different competencies. One may specialize in integrating LLM into applications; another may focus on developing predictive models; a third may concentrate on governance, risk, and evaluation.
Before starting a selection process, it is advisable to define the type of system that will be developed, its maturity level, the available data sources, the architecture where it will be deployed, and the responsibilities the professional will assume. This definition allows for determining seniority and technical experience without creating excessively broad positions.
When the need requires incorporating external specialization, IT outsourcing models allow for expanding technological capacity with professionals suited to the project and existing architecture. Selection can thus focus on the specific competencies needed for each AI initiative.
2027 will consolidate new specializations around artificial intelligence
The evolution towards 2027 points to a more specialized market. AI engineers and machine learning engineers have demand supported by labor trends extending to 2030, while generative AI engineering is growing alongside the business adoption of generative models.
Prompt engineering will remain a relevant competency, although its integration within broader technical functions will likely prove more significant than considering it always as an independent profession. AI governance and AI auditing respond, on the other hand, to a different need: controlling systems that are gaining greater presence in operations and business decisions.
For organizations, this specialization requires linking each hiring to a specific technological objective. If your company is developing artificial intelligence projects and needs to determine what experience to incorporate, you can schedule a call with LaTeam to analyze the appropriate profile and level of specialization.
