jberk in BlogInsights

AI Roles: Who Do You Really Need for Implementing AI in Your Business?

ai roles

While several research studies indicate that AI spending is projected to exceed USD 749 billion by 2028, there’s still an expected 50% shortfall in AI talent. With the demand for AI and ML professionals rising, this could be a huge problem for many organizations, which in many cases will have to rescaling or supplement some AI positions or even operate with smaller teams.

In this context, some AI roles will be more essential than ever to start implementing this technology. But what exactly are these key positions? Which of them are most affected by this lack of talents? Is it possible to replace them with other developers with similar skills?

In today’s article, we’ll see the key positions needed to start with AI and how to face the talent shortage without compromising the final product.

What Are the Key AI Roles for Business?

So, AI development teams vary depending on the size, needs, and goals of an organization. In fact, more ambitious AI projects naturally require more specialized roles, though not necessarily larger teams. Let’s take a company developing a basic AI-powered chatbot and one building an advanced AI-driven fraud detection system as an example.

AI roles vary depending on the size, needs, and goals of an organization

A basic AI-powered chatbot might require a larger team of general software engineers, backend developers, and data scientists handling various aspects like NLP, integrations, and user experience. Meanwhile, an AI-driven fraud detection system could be built by a smaller but more specialized team—perhaps just a ML engineer, a data scientist specializing in anomaly detection, and a security expert.

So, instead of scaling headcount, some projects can rely on highly specialized skills to tackle complex problems efficiently. However, certain foundational positions are essential for developing and deploying these systems.

In the following sections, we will outline the key AI roles for any business.

1. ML Engineer

The first basic AI roles are ML engineers, the backbone of AI development as the responsible for turning data-driven models into functional AI solutions. They basically bridge the gap between theoretical research and practical application, ensuring models can operate efficiently in real-world scenarios.

ML engineers are responsible for turning data-driven models into functional AI solutions

Working closely with data scientists and AI architects, they optimize models for performance and scalability, often deploying them in cloud or edge environments. So, we can summarize their basic tasks as it follows:

Analyze and interpret large datasets to identify trends and patterns.

Develop ML models for predictive analytics.

Clean, preprocess, and structure data for AI applications.

Collaborate with ML Engineers to ensure model accuracy and efficiency.

2. Data Scientists

As the second of the AI roles, data scientists focus on extracting meaningful insights from vast datasets. They not only develop ML models but also analyze patterns and trends that drive business decisions. So, in short, it’s thanks to this growing field that customer needs are predictable, fluctuating demands can be forecasted, and potential supply and delivery disruptions can be identified before they magnify.

Data scientists focus on extracting meaningful insights from vast datasets

Their work often involves hypothesis testing, feature engineering, and data visualization to make AI models more interpretable. While they may collaborate with ML engineers, their primary goal is to experiment with algorithms and assess the impact of data-driven strategies. So, they tasks can be summarized as it follows:

Design and implement data pipelines to collect, store, and process data.

Optimize databases and ensure efficient data retrieval.

Work with cloud platforms (AWS, GCP, Azure) to manage AI-related data workflows.

Ensure data quality, integrity, and security.

3. Data Engineer

Data engineers are the ones that ensure AI teams have clean, reliable, and well-structured data to work with. Basically, since AI models heavily depend on large-scale data, these engineers build and maintain the infrastructure that facilitates smooth data collection, processing, and storage.

Data engineers focus on data cleaning and structuring, making sure that AI teams have clean, reliable, and well-structured data to work with

They share some tasks with data scientists, but the latter focus more on data analysis, while engineers concentrate on data cleaning and structuring. In short, without data engineers, AI teams would struggle with fragmented, inconsistent, or inaccessible data. This way, they must:

Design and implement data pipelines to collect, store, and process data.

Optimize databases and ensure efficient data retrieval.

Work with cloud platforms (AWS, GCP, Azure) to manage AI-related data workflows.

Ensure data quality, integrity, and security.

4. AI Architect

Another of the AI roles is the AI architects, which are responsible for designing the high-level structure of AI systems, ensuring they align with business objectives and technical requirements. They make strategic decisions about which frameworks, platforms, and tools to use for AI development.

Their expertise in system scalability, cloud deployment, and AI infrastructure ensures that AI models can be seamlessly integrated into existing IT ecosystems, working closely with C-level executives to define the long-term AI strategy. So, their tasks are:

Defining the AI system architecture, including model selection and deployment strategies.

Ensuring AI solutions align with business goals and technical infrastructure.

Overseeing the scalability, security, and performance of AI systems.

Guiding ML Engineers and Data Scientists on best practices for AI development.

5. AI Trainer (for NLP and Conversational AI)

AI trainers are one of the AI roles who specialize in fine-tuning models, particularly those related to NLP and conversational AI. In other words, since AI-driven chatbots and virtual assistants need to understand human language, trainers play a key role in teaching these systems how to interpret context, intent, and sentiment.

Their work is crucial in making AI more human-like and effective in customer service, automation, and digital assistants. Their tasks include:

Label and preprocess text data for training NLP models.

Fine-tune AI models to understand context, intent, and sentiment.

Improve conversational AI through reinforcement learning.

Work with UX/UI designers to enhance chatbot interactions.

6. AI DevOps Engineer

AI DevOps engineers focus on streamlining the deployment and monitoring of AI models in production environments. Unlike traditional DevOps, they must handle large-scale AI workloads, automating AI pipelines, and ensure models are updated regularly.

AI DevOps engineers focus on streamlining the deployment and monitoring of AI models in production environments

Their main occupation of these engineers is to prevent AI models from degrading over time by continuously retraining and redeploying them as new data becomes available. So, they oversee:

CI/CD pipelines implementation for AI models.

AI model deployment automation and scaling in cloud environments.

AI system performance and troubleshoot issues monitoring.

Ensuring AI applications meet reliability and security standards.

7. AI Research Scientist

AI research scientists are one of the most peculiars AI roles. They must push the boundaries of AI by developing new algorithms, architectures, and methodologies. They often work in academic settings or research divisions of tech companies, focusing on cutting-edge AI innovations like reinforcement learning, GenAI or AI agents.

AI research scientists lay the foundation for future AI advancements and breakthroughs

So, while they may not always be involved in day-to-day AI deployment, their contributions lay the foundation for future AI advancements and breakthroughs. So, they have to:

Conduct research on AI and ML techniques.

Develop new AI models and improve existing ones.

Publish findings in academic journals and conferences.

Collaborate with ML Engineers to bring research into real-world applications.

8. AI UX/UI Designer

AI UX/UI designers ensure that AI-powered applications provide a seamless user experience. Since AI solutions can sometimes feel complex or unintuitive, they must work to make interactions more natural and accessible.

Whether designing chatbot interfaces, recommendation systems, or voice assistants, their task is to optimize how users interact with AI while maintaining clarity and usability. Therefore, their roles can be summarized as it follows:

Design of interfaces for AI-driven applications, ensuring ease of use.

Develop of AI interaction models for chatbots and virtual assistants.

Conducting user research to improve AI system usability.

Work with AI trainers and data scientists to optimize user interactions.

9. AI Cloud Engineer

Cloud engineers, as it names indicate, focus on leveraging cloud computing to support AI workloads. Basically, since AI models require significant computational power and storage, they must ensure that AI applications are efficiently deployed on cloud platforms like AWS, Google Cloud, or Azure.

So, these AI roles are crucial for organizations that want to deploy AI at scale without investing heavily in on-premise infrastructure, as they oversee:

Cloud costs optimization.

Virtual AI environments management.

AI workloads are scalability and security.

10. AI QA Engineer

QA Engineers play a critical role in ensuring AI apps function correctly before deployment. However, unlike traditional software testing, AI QA involves validating AI model accuracy, performance, and fairness. They basically identify biases, inconsistencies, or security vulnerabilities in these systems, ensuring compliance with industry standards and preventing models from making incorrect or unethical decisions.

11. AI Support Specialist

Finally, support specialists provide ongoing technical assistance to businesses using AI solutions. Whether troubleshooting errors, monitoring AI system performance, or assisting users, they ensure that apps function smoothly post-deployment.

Their expertise is especially important in organizations where AI is customer-facing, such as virtual assistants or automated decision-making systems. So, their tasks are:

Monitoring AI system performance and resolving issues.

Providing end-user support for AI-driven applications.

Documenting common AI errors and solutions.

Working with developers to refine AI functionality based on user feedback.

How are companies overcoming the AI talent gap?

There are several ways in which modern businesses are facing the lack of talents and filling these key AI positions. However, we can summarize the most used methods as it follows:

Reskilling & Upskilling: Many organizations opt for training their existing developers and data teams in AI through courses, certifications, and hands-on projects. This is usually best for long-term AI adoption but requires time.

Staff Augmentation: Other option is to bring in AI experts on-demand to complement your existing team.

Dedicated AI Teams: Partner with an AI-focused service provider that offers end-to-end AI development and deployment is also a good option. This is ideal for companies that want fast results with minimal risk.

AI Consulting & Advisory: Work with AI consultants to design strategy, choose the right technologies, and set up best practices before hiring a full team.

Managed AI Services: Outsource AI operations to a third-party provider that handles development, deployment, and ongoing optimization, ensuring AI success without internal overhead.

Bear in mind that each option depends on your business goals, AI maturity, and budget. On the other hand, is also essential to know your project needs and your business goals in detail to choose the right approach and ensures AI success without wasting time or resources.

However, the most important thing to do when facing a talent gap is to avoid falling in one of the most common mistakes and AI project failure causes: to delegate these AI roles into general developers. But let’s see this in the next section.

You Need a Trusted AI Partner—Not Just Any Developer

So, although these AI roles are key for an AI project success, businesses face a talent shortage that many times force them to fill these positions with general developers instead of AI specialists. However, while this is motivated by the necessity rather than strategic purposes, we must warn that it’s not the answer.

While general developers may have coding and data skills, they often lack the expertise needed to build, deploy, and scale AI solutions effectively. This leads to misaligned strategies, poor implementation, and missed opportunities to maximize AI’s potential. However, the key reasons because general developers fall short in AI projects can be summarized as it follows:

They Know How to Code, But Not How to Train AI

General developers can write software, but AI development requires model training, data preprocessing, and algorithm optimization, which are entirely different skill sets.

They Work with Data, But Not AI-Ready Data

A typical developer may handle databases, but AI models need structured, cleaned, and bias-free datasets to function correctly. Poor data preparation leads to inaccurate or biased AI results.

They Can Build Apps, But Not Scalable AI Systems

Writing an AI-powered feature isn’t the same as deploying an AI system at scale. AI demands continuous learning, monitoring, and tuning, which general developers often overlook.

They Lack AI-Specific Business Alignment

AI must solve business problems, not just technical ones. Without AI experts, companies risk building overcomplicated, ineffective, or impractical solutions that fail to deliver real value.

They Aren’t Trained for AI Risks & Compliance

AI comes with ethical, legal, and security challenges. Without specialists, businesses risk biased models, regulatory violations, and AI failures that could damage their reputation.

So, in short, success with business AI starts with the right professionals. But, with AI talent in short supply, partnering with a trusted hiring expert is far better than relying on general developers. This approach ensures specialized expertise, prevents costly technical missteps, and maximizes your IT budget long-term.

But, ultimately, it helps you avoid one of the biggest failure points in AI projects—building teams without the right skills to develop and deploy AI effectively. And at Inclusion Cloud we can help you start your AI adoption with the right talents and tools. Let’s connect and start your AI deployment today!

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