SpeShu.AI explained how to find work in AI amid growing job vacancies in Russia

SpeShu.AI released a quick guide to job search in AI amid a notable rise in demand for such skills in Russia. In the first quarter of 2026, employers posted over 16.5 thousand job openings that required AI skills or willingness to learn neural networks — this is 2.7 times more than a year ago. At the same time, the digital and IT market itself remains tough: many positions receive hundreds of responses, and hiring moves slowly.

Demand is Growing Fast

The figure of 16.5 thousand vacancies shows that AI has stopped being a narrow competency only for ML engineers and researchers. Employers' phrasing has become broader: companies need people who can apply neural networks in marketing, analytics, development, support, content, and operational processes. In many job postings, deep experience in data science is not required, but rather a willingness to quickly master tools and integrate them into current work. This lowers the entry barrier, but at the same time raises the demands for the candidate's practical utility.

Growth in the number of vacancies does not mean the market has become easy again for job seekers. Quite the opposite: companies are more actively seeking ways to increase team efficiency through AI tools, but they hire cautiously and wait for clear results. Therefore, 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: completing tasks faster, saving budget, improving quality, or delivering more work with the same resources.

Competition is Getting Tougher

On the digital and IT market, a paradox persists: vacancies collect 100–200 responses, but a position can remain open for a long time. Usually this means there is a gap between supply and demand sides. There are many job seekers, but a significant part of resumes look identical: everyone writes about ChatGPT, automation, and prompts, but they don't show what exactly was done and what effect it had. For an employer, such responses quickly turn into noise, especially at the first stage of selection.

There is a second problem: companies themselves often don't yet know how to formulate who exactly they are looking for. Behind the title "AI specialist" could be anyone — from a content manager who will speed up material production to a product analyst capable of automating routine work within the team. In such a situation, candidates who translate their skills from the language of tools to the language of tasks win. Not "I can work with neural networks," but "I reduced report preparation time," "I built a chatbot for internal requests," "I accelerated research and content pipeline."

How to Search for Jobs Now

The practical conclusion for a job seeker is simple: the market is more willing to hire not "AI enthusiasts," but people capable of integrating AI into an already existing profession. Abstractions don't work as well as short case studies, clear numbers, and specialization. If a candidate has a basic profession — development, marketing, design, sales, analytics, HR — AI acts as an amplifier, not a separate magical role.

• Show 2–3 case studies where AI saved time, money, or manual actions.
• Tailor your resume to a specific job posting rather than sending one generic text to everything.
• Emphasize the pairing "profession + AI," 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 to positions where you see a real business task, not a vague formulation about "neural network expertise."

A separate signal for newcomers: willingness to learn neural networks has now itself become part of the requirements. This means entry into the segment remains open, but only for those who learn quickly and immediately reinforce skills through practice. For experienced specialists, the conclusion is similar: what is valued is not just experience, but the ability to reshape your role for a new toolkit. The winner is not the most prominent profile, but the one with the best route mapped from task to result.

What This Means

The AI job market is expanding, but it has already moved past the stage where it was enough to write about interest in neural networks in your resume. For job seekers, this is a signal to shift from general statements to case studies and applied specialization. For companies — a reminder that demand for AI skills is growing faster than the ability to precisely formulate expectations for new roles.