SpeShu.AI explains how to find an AI job as Russian vacancies surge

SpeShu.AI has published a quick guide to finding an AI job amid a noticeable rise in demand for such skills in Russia. In the first quarter of 2026, employers posted more than 16.5k vacancies that required AI skills or a willingness to learn neural networks, 2.7 times more than a year earlier. The digital and IT market itself remains tough, though: many positions receive hundreds of applications, and hiring moves slowly.

Demand is growing fast

The figure of 16.5k vacancies shows that AI is no longer a narrow skill set reserved for ML engineers and researchers. Employers now cast a wider net: companies need people who can apply neural networks in marketing, analytics, development, support, content and operations. Many postings do not ask for deep data science experience, only a readiness to pick up tools quickly and build them into current work. That lowers the barrier to entry, but it also raises the bar for the practical value a candidate brings.

More vacancies do not mean the market has become easy for job seekers again. If anything, the opposite is true: companies are looking harder for ways to make their teams more efficient with AI tools, but they hire cautiously and expect clear returns. So the mere fact that a candidate uses neural networks no longer gives an automatic advantage. Employers want to see not interest in the topic but a link between the skill and a specific business result: getting tasks done faster, saving budget, improving quality or producing more work with the same resources.

Competition has got tougher

The digital and IT market still has a paradox: vacancies attract 100–200 applications each, yet a position can stay open for a long time. This usually means there is a gap between supply and demand. There are plenty of job seekers, but a large share of résumés look the same: everyone writes about ChatGPT, automation and prompts, yet does not show what exactly was done and what effect it had. For an employer, such applications quickly turn into noise, especially at the first screening stage.

There is a second problem: companies themselves often still cannot say exactly who they are looking for. The title "AI specialist" can cover anyone, from a content manager who speeds up material production to a product analyst who can automate routine work inside a team. In this situation, the winners are candidates who translate their skills from the language of tools into the language of tasks. Not "I can work with neural networks" but "cut report preparation time," "built a chatbot for internal requests," "sped up the research and content pipeline."

How to look for a job now

The practical takeaway for job seekers is simple: the market prefers not "AI enthusiasts" but people who can build AI into a profession they already have. What lands fastest is not abstract claims but short case studies, clear figures and a specialization. If a candidate has a core profession (development, marketing, design, sales, analytics, HR), AI works as an amplifier, not as a separate magic role.

• Show 2–3 cases where AI saved time, money or manual steps.
• Tailor your résumé to each vacancy instead of sending one generic text to everyone.
• Emphasize the "profession + AI" combination, for example "marketer with AI automation" or "analyst with LLM pipelines."
• Add a simple portfolio: Notion, PDF, GitHub, a spreadsheet or a landing page with examples of tasks and results.
• Apply where a real business problem is visible, not to a vague posting for a "neural network expert."

There is a separate signal for newcomers: willingness to learn neural networks has itself become part of the requirements. The segment stays open, but only to those who learn fast and immediately reinforce their skills in practice. The takeaway is similar for experienced specialists: what is valued is not just years of service but the ability to rebuild one's role around a new stack of tools. The winner is not the loudest profile but the one with the best route from task to result.

What this means

The AI job market is expanding, but it has moved past the stage when it was enough to mention an interest in neural networks on a résumé. For job seekers, this is a signal to move from generic words to case studies and applied specialization. For companies, it is a reminder that demand for AI skills is growing faster than the ability to define expectations for new roles precisely.