By 2025, more than 97 million new roles will arise between man and machine.” – World Economic Forum
And this is no longer a distant future. Artificial intelligence is reshaping the job market in real time. Every industry-from healthcare to logistics, from retail to finance-is experiencing a silent but profound revolution: new skills, new job titles, new mindsets are needed.
Demand is growing. But skills are in short supply.
In this article, we will take a detailed look at what professionals are emerging in the AI (Artificial Intelligence) landscape, what hard and soft skills are most in demand, and how you can start building a competitive profile in this new scenario today.
Whether you are considering a career change or planning hiring for your business, this guide will give you a clear road map for not falling behind.
The new professional figures born from artificial intelligence
AI Prompt Engineer
In the landscape of new AI professionals, the role of theAI Prompt Engineer is emerging as one of the most crucial. This figure is responsible for writing and optimizing textual commands, known as “prompts,” to interact effectively with artificial intelligence language models such as ChatGPT, Claude, or Bard. Goals can range from generating consistent content for marketing, to better customer service management, to automated creation of technical or sales documentation. To excel in this role requires soft skills that blend Natural Language Processing (NLP), formal logic, and strong linguistic creativity.
Prompt Engineering has become a mainstay in numerous application areas. In marketing, for example, it enables the generation of SEO-optimized texts, persuasive emails or product descriptions, reducing time and costs. In customer service, on the other hand, well-constructed prompts improve the efficiency and quality of automated chatbot responses, increasing customer satisfaction. The best AI Prompt Engineers know how to balance stringent input with informative and consistent output, leveraging the language model’s ability to “complement” human thinking.
Ethic AI Specialist
Another of the growing AI professionals is theEthic AI Specialist. These are experts who can critically evaluate the ethical, legal and social impacts of AI-based solutions, particularly in optimizing algorithms to eliminate bias or discrimination. The skills of an Ethic AI Specialist include moral philosophy, European and international AI regulation, and Fair Machine Learning, a discipline that studies algorithmic fairness in predictive systems.
The importance of this figure is steadily increasing, especially for companies that want to maintain a competitive advantage while complying with ESG principles and future regulatory requirements, such as the European AI Regulation (AI Act). Knowing how to integrate an ethical approach already at the algorithm design stage not only protects the company’s reputation, but also reduces the risk of incurring penalties and reputational problems. Especially in the banking, healthcare or HR sectors, where sensitive data and automated decisions can have significant impacts, this figure assumes a strategic role.
Data & AI Product Manager
The Data & AI Product Manager is a critical link between business needs and technology solutions based on data and predictive algorithms. This figure must possess skills in both data strategy, user research and agile project management, and is called upon to lead the design of intelligent products by leveragingAI-driven decisioning. It functions as a true “bridge” between strategic stakeholders and technical development teams.
A good product manager in AI understands both the logic of machine learning models and the needs of the end user. He knows how to turn a product vision into an implementable roadmap, taking into account technological constraints, data quality, and sustainability of the solution over time. He can, for example, coordinate the launch of a recommendation system in e-commerce, where user experience, personalization, and algorithmic performance must coexist in balance. The ability to work in interdisciplinary environments is one of the most in-demand skills for this figure.
AI Trainers and Data Modelers
In any AI project, data quality is crucial. AI Trainers and Data Modelers are responsible for guiding the training phase of the models, ensuring that the datasets are clean, consistent and representative. They are responsible for labeling (data labeling), semantic structuring of inputs and data governance, which are essential elements in providing the machine learning system with a solid and effective foundation.
Core skills include computational linguistics, knowledge of best practices for handling big data, and familiarity with annotation tools. Their work is particularly strategic in regulated contexts, such as healthcare, finance, or law, where data errors can result in decisions with practical and legal consequences. Even in multilingual projects, their intervention is central to ensuring that the model understands, processes, and responds appropriately in different idioms.
AI Operations (MLOps) Engineer
The figure of theAI Operations Engineer, or MLOps Engineer, combines DevOps skills with know-how in themachine learning ecosystem, and is responsible for managing, monitoring and maintaining AI models in a production environment. This profession is becoming indispensable especially in AI-centric start-ups and scale-ups, where speed and operational efficiency make all the difference.
AI skills required include containerization (Docker, Kubernetes), continuous deployment (CI/CD), and monitoring of the entire ML lifecycle, from the training phase to deployment to production. These engineers are called upon to ensure that models are performant, up-to-date, and resilient to changes in input data. A well-structured MLOps prevents the phenomena of “model drift” and “data skew,” which risk compromising the effectiveness of the AI solution over time. Automation of training and deployment pipelines is key to their operation.
Hard skills essential
Among the most in-demand technical skills in the AI world we find an indispensable set of hard skills, without which it is difficult to remain competitive. Prominent among these are:
- Python, R, and SQL: Fundamental languages for programming and data manipulation. Python, in particular, is the de facto standard in AI development thanks to libraries such as TensorFlow, Scikit-Learn and PyTorch.
- Machine Learning and Deep Learning: It is crucial to know how to design neural networks, choose appropriate algorithms for the type of data, and interpret the behavior of the model.
- Cloud Computing (AWS, Azure, GCP): Major cloud platforms offer specialized tools for training and deploying AI models, enabling scalability, reliability and process security.
Professionals who master these skills have access to well-paid roles with high demand in the market.
| Technical Skills | Principal Use | Main tools |
|---|---|---|
| Python | Scripting, model development | Jupyter, scikit-learn, PyTorch |
| SQL, R | Data analysis and cleaning | BigQuery, PostgreSQL, RStudio |
| ML/DL | Predictive model creation. | TensorFlow, Keras, PyTorch |
| Cloud Computing | Scalable deploy and project management | AWS SageMaker, Azure ML, Google AI Hub |
Soft soft soft skills
Technical skills are critical, but they become powerful only when accompanied by a set of soft soft soft skills that enable AI professionals to interact in complex and ever-changing environments:
- Critical thinking and problem solving: Fundamental to analyzing ambiguous scenarios, assessing data quality, and deciding how to intervene on a fallacious model.
- Adaptability: AI technologies evolve rapidly. Maintaining a flexible mindset is key to preventing skills from becoming obsolete.
- Effective communication: Essential to “selling” a technology often perceived as abstract. Knowing how to explain the impact of an AI model to non-technical stakeholders is a leader quality.
The effectiveness of an AI project is measured not only in the results, but in the ability to communicate, interpret and correlate them with business objectives.
Certifications and recommended pathways
For those wishing to enter the world of artificial intelligence-related professions, there are a number of certification and training pathways that either give a solid foundation or allow existing skills to be upgraded.
- Online AI course by Google and IBM: Two of the most influential players in the field, they offer online courses covering the fundamentals of data science, machine learning and ethical AI, with hands-on projects.
- Microsoft AI Engineer Certificate: Ideal for those who want to specialize in the Azure ecosystem, it provides comprehensive training on creating and deploying models.
- University Master’s degrees: Many Italian and European universities have master’s degrees such as the one at the Milan Polytechnic or Bocconi, geared toward both students and professionals.
Investing in these pathways increases one’s expendability in the labor market and certifies skills that are increasingly in demand today.
How to constantly update yourself
In the field of artificial intelligence, constantly updating is not an optional extra, but a necessity. Tools, methodologies and even standards change with such speed that staying up-to-date becomes an integral part of the job. For this reason, it is recommended that you incorporate the following activities into your routine:
- Industry Newsletter: For example, Azeem Azhar’s “Exponential View” and MIT’s “The Algorithm” offer weekly insights, international case studies and strategic forecasts.
- Active communities on Discord and GitHub: Getting in touch with other developers and professionals helps to solve technical problems, but also to exchange practical insights and suggestions on new frameworks.
- AI competitions: Platforms such as Kaggle and DrivenData allow skills to be honed on real projects. Exposure to real challenges enhances AI skills, increases portfolios, and attracts HR attention.
Staying curious, participating in international conversations and practicing continuously is the best investment for an AI-proof future.
Artificial intelligence is no longer an option. It is a reality shaping every industry today.
Professional figures change, but the real question remains: are you ready to evolve with them?
Don’t wait for “top-down” change. Invest now in your education, watch trends, experiment, train.
Enter the new AI economy as a protagonist, not a spectator.
