Tech hiring pivots to AI and infrastructure as layoffs reshape the job market

After two turbulent years of layoffs, the technology job market is starting to look different from the one that peaked in 2021. The headline numbers still sound gloomy, but under the surface hiring is shifting toward new skill sets and team structures.

Companies are reducing some roles while expanding others, especially in artificial intelligence, data platforms and core infrastructure. For workers and employers alike, the reshuffle is changing how careers are planned, projects are staffed and products are delivered.

From hypergrowth to “prove it” mode

Mass layoffs that began in late 2022 have affected tens of thousands of workers at big names like Meta, Amazon, Google and Microsoft, as well as many smaller firms. Public layoff trackers show that cuts have continued into 2024, but at a slower pace than the initial wave.

Analysts point to a common pattern: the easy money era of near-zero interest rates encouraged aggressive hiring, then higher borrowing costs forced a pivot to profitability. That shift is still playing out. Teams that focused on rapid expansion, experimental products or overlapping responsibilities have been trimmed or reorganized.

At the same time, companies are not simply freezing all hiring. Many are redirecting budgets toward roles that can demonstrate clear revenue impact, cost savings or strategic value. That is helping some specialists find new opportunities faster than broad generalists.

AI skills move from edge case to core requirement

One of the sharpest changes is in how AI skills are valued. A few years ago, machine learning roles were often concentrated in research groups or specialized data science teams. Now, many job postings for software engineers, product managers and designers list AI familiarity as a preferred or required skill.

Demand is highest for engineers who can work with large language models, retrieval systems and production ML platforms, rather than only building proofs of concept. Companies want people who can integrate AI features into existing products, handle safety and compliance issues, and manage costs on cloud-based AI infrastructure.

There is also growth in supporting roles: data engineers to build reliable pipelines, MLOps specialists to handle training and deployment workflows, and developers who can optimize AI-heavy applications for latency and efficiency.

Platform and infrastructure teams gain influence

Another clear trend is the rise of internal platform and infrastructure teams. As organizations seek to do more with fewer people, there is renewed interest in centralized tooling that lets product teams move faster without duplicating effort.

Roles with titles like “platform engineer,” “developer experience engineer” and “site reliability engineer” are increasingly visible in job boards and company org charts. Their mandate is to maintain reliable systems, standardize best practices and automate work that used to be handled manually by each individual team.

Cloud expertise remains essential, but expectations have matured. Employers are looking for people who can design cost-aware architectures, negotiate vendor sprawl and rationalize legacy systems rather than simply “move everything to the cloud.”

What this means for job seekers

For individuals navigating the current market, the shift brings both challenges and opportunities. Competition is intense for junior positions and generalist roles, especially in popular locations, but candidates with the right mix of experience can still move quickly between jobs.

Several patterns stand out in postings across North America and Europe:

• Strong fundamentals still matter:core skills in algorithms, networking, databases and security appear frequently, especially for infrastructure and platform work.
• Applied AI beats theory:being able to use existing models, evaluate them and integrate them into products often counts more than building new models from scratch.
• Systems thinking is valued:employers want people who understand how code, data, operations and business metrics connect.

Retraining is becoming common. Many back-end engineers are learning more about machine learning pipelines, while data analysts move toward analytics engineering and product analytics. Online courses and open-source projects are popular ways to demonstrate up-to-date skills.

Remote and hybrid work reshape where talent is found

The rise of remote and hybrid work during the pandemic has not fully reversed, especially in technology and digital-first roles. Some large firms are pushing for more days in the office, but most job boards still feature a substantial share of remote or partially remote openings.

This is redistributing opportunities across regions. Candidates in smaller cities or outside traditional tech hubs are now applying for roles at multinational companies, while employers use distributed teams to reduce costs and widen the talent pool.

However, remote hiring also increases competition. A mid-level engineer applying for a remote position may be competing with hundreds of candidates across several countries. Strong portfolios, clear communication skills and specific domain knowledge can help applicants stand out.

Startups and large firms take different hiring bets

Larger technology companies tend to focus on platform investments, AI infrastructure and cost control. Their size lets them centralize specialized skills and build long-term internal tools, even while trimming other departments.

Startups, by contrast, often look for multi-skilled hires who can wear several hats. One role might cover product management, customer discovery and basic data analysis. Another might combine software development with DevOps responsibilities.

Early-stage AI startups are particularly active in hiring applied researchers and engineers with experience in model fine-tuning, vector databases and human-in-the-loop evaluation. Funding conditions remain tougher than in 2021, but investors are still backing teams that can show early traction and a path to sustainable margins.

Preparing for the next phase of tech work

While no one can predict exactly how long the current reshuffle will last, some themes appear durable. AI is becoming part of everyday tools rather than a niche speciality. Infrastructure and platform work is more central to product success. Remote collaboration is now a standard skill, not a temporary workaround.

For workers, that suggests a practical approach: treat AI and automation as tools to master, not threats to avoid; build skills at the intersection of software, data and infrastructure; and maintain a portfolio of real projects that show measurable outcomes.

For employers, the question is how quickly hiring plans can adapt to these realities. Those that align job descriptions, training and internal mobility with the new mix of AI, platform and product work are likely to have an edge as the next cycle of innovation begins.