The AI-era talent market: the evolving role of engineers and the need for team adaptation
Andrii Yavorsky
Vice President of Strategy and Technology, GlobalLogic
AI is fundamentally changing the job market. We are seeing a paradox: on one hand, AI makes it possible to overcome the shortage of specialists by automating repetitive tasks. In many industries, it is becoming possible to shift routine processes to systems that do not make mechanical errors and can work without days off.
On the other hand, the demand for highly skilled professionals with a deep understanding of AI is growing faster than the market can train them. A specialist who only writes code no longer meets business needs. Companies increasingly need people who can understand how models work, how to integrate them into real-world processes, and how to evaluate the impact of AI implementation on the product.
Traditional programming is losing its monopoly on the "engineer" competency. The focus is shifting from syntax to context: what matters is not how a solution is written, but whether it solves a business problem. We will discuss how these changes affect the job market and what exactly companies need to change in their operating models to successfully adapt to the new AI reality.
A New Shortage in the Job Market
The development of AI is creating a "split talent shortage" paradox in the job market.
- On one hand, AI serves as a powerful mechanism for overcoming shortages at the entry and mid-levels. This is becoming especially noticeable in post-industrial countries. The implementation of AI agents allows for the automation of both physical and cognitive routine tasks: customer support, request processing, quality control, and working with standardized documents, accounting records, and databases. What previously required hundreds of hours of manual labor can now be handled by a synthetic workforce.
- On the other hand, we see a growing demand for highly skilled professionals with a deep understanding of model architecture, their integration into the software development life cycle, and expertise in context engineering. The world simply cannot keep up with training such specialists, which creates a severe talent shortage at the high-seniority level.
Consequently, AI is replacing the need for a less-skilled workforce while simultaneously creating a shortage of highly skilled talent. How does this affect the role of the engineer?
The impact of AI on the engineer's role: coding is no longer enough
The previous wave of AI interest was accompanied by a surge of attention to prompt engineering. The ability to correctly phrase a request seemed to be the key to success. However, prompt engineering is already becoming outdated — the future belongs to context engineering. This means moving beyond working with phrases and instead shaping the conditions under which a model makes decisions: the right data, the necessary domain information, and a controlled environment.
Currently, the most valuable specialists are those who understand the inner workings of LLMs: the architectural principles behind them, how models are adapted for specific tasks, and what happens when they interact with corporate systems. We are talking about a new kind of professional: experts who can bridge technical expertise with business insight. In this approach, the engineer's role is not just about writing code, but about designing the logic where AI autonomously generates part of the solution.
The shift in the job market and changing engineering requirements are forcing businesses to undergo a comprehensive transformation. Unfortunately, most companies today are not ready for it. AI investments remain chaotic, and the majority of solutions yield neither revenue nor resource savings. So, what should companies do first?
A shift in methodology
The problem with AI implementation lies not only in the shortage of specialists but also in outdated development approaches. Companies often apply classic methodologies where they simply don't work. The traditional software development life cycle is ill-suited for AI — it must change completely. Models continue to learn and require constant support. Consequently, the moment of release is no longer the finish line — it becomes the starting point.
Companies need new ways of organizing work that account for the unique nature of these models. This involves a staged implementation, continuous adjustment, and adaptation to real-world tasks through pilots and transparent mechanisms for measuring impact. For instance, businesses must move away from trying to implement AI in one giant leap — investments should be structured as safe, scalable, and economically viable stages.
Rethinking HR approaches and team structures
The surging demand for highly skilled professionals with a deep understanding of AI is forcing a rethink of traditional HR approaches. Since the market lacks the necessary number of people, companies must create internal schools and upskilling programs. It is also worth focusing on developing new role systems and, more importantly, overcoming internal resistance — the fear of job loss due to automation.
It is already becoming clear that AI is reshaping team structures. Companies must learn to organize work in a way that allows people and their synthetic colleagues to complement each other.
A shift in models
Moving toward renewable models instead of one-off projects is becoming critical. This solves the problem of rapid technological obsolescence. Such an approach changes the way we think about AI products: they shouldn't just deliver a one-time result — they must evolve alongside the business and the market. In practice, this means more than just "developing an AI feature"; it means building a framework where engineers constantly return to the product to update data, monitor changes in system behavior, and so on.
This is the exact logic behind GlobalLogic’s AI approach. We don’t view AI as a one-time integration that can be handed over to a client and forgotten. On the contrary, we build our work as a long-term partnership: we identify priority processes and create an implementation roadmap. From there, the team regularly revisits the solutions to refine, improve, and scale them in response to business needs.
We describe this approach simply: AI is not a product, it is a process. Solutions must live, adapt, and build cumulative impact. This requires constant ongoing support. Such a model helps avoid typical risks: the loss of expertise, system imbalance due to data shifts, or performance degradation under new operating conditions.
This led us to develop VelocityAI — a suite of AI tools for software development across all stages of the lifecycle. It enables the creation of interconnected agent systems that collaborate within workflows. This architecture is already live inside GlobalLogic, where 20+ production agents assist in drafting technical Statements of Work (SOW), accelerating R&D. The results speak for themselves:
- We have developed over 200 AI and GenAI solutions, as well as agentic systems;
- We have built a team of over 1,000 AI engineers, 500+ data specialists, and 7,000 operational resources who support AI in production environments;
- We have created 20+ GenAI accelerators, an enterprise-grade AI platform, and an execution environment for agentic solutions.
Thus, the key takeaway for businesses is that AI should not be a one-time development but a managed ecosystem that is constantly updated and scaled. Only then does the technology remain relevant, avoiding the trap of becoming "last year's project" and delivering results in the long run.