In LinkedIn’s latest Fastest Growing Jobs report, which ranks the fastest-growing jobs in the U.S. over the past three years, AI and infrastructure jobs make up the majority of IT jobs.
The 2026 list shows continued demand for technical talent to support the development, deployment, and operations of AI, as well as data center and quantitative roles related to infrastructure and analytics.
AI engineers (also known as machine learning engineers) rank as the fastest growing job overall. These experts design and implement AI models used for tasks such as prediction and decision-making.
According to LinkedIn, the most common skills for AI engineers include LangChain, Search Augmentation Generation (RAG), and PyTorch. This role is most common in technology, IT services, and business consulting, with the highest concentration of jobs in San Francisco, New York City, and Dallas.
AI consultants and strategists are also closely involved and are focused on helping organizations plan and implement AI initiatives. The core of their work is aligning AI technology with business objectives. Common skills include large-scale language models, machine learning operations (MLOps), and computer vision.
These roles are concentrated in the technology and consulting sectors, with the highest hiring activity in San Francisco, New York City, and Boston. The professionals who fill these positions often have software engineering, product management, or founder backgrounds.
Ali Gohar, chief human resources officer at Software Finder, says that companies are now not only valuing theoretical knowledge about AI, but also the ability to implement it.
“The most in-demand skills that employers are prioritizing include hands-on programming in Python, familiarity with modern machine learning libraries such as PyTorch and TensorFlow, and the skills needed to deploy and manage models in production,” he explains.
Additionally, MLOps skills such as model versioning, monitoring, cost optimization, and governance are considered minimum requirements rather than differentiators for AI-related jobs.
“It is important for an AI consultant to combine these requirements with the skills to identify problems, determine what level of AI should be used for a particular problem, and communicate trade-offs effectively,” Gohar says.
AI and machine learning researchers also feature prominently on the list. These roles include designing and testing new models and algorithms to advance AI systems.
Some of the most commonly cited skills include PyTorch, deep learning, and computer vision. AI/ML researchers are typically employed by technology companies, higher education, and research services.
The report notes that jobs are concentrated in major technology hubs such as San Francisco, New York City and Boston, with many moving from data science and machine learning engineering jobs.
Data annotators (also known as content analysts) who support AI development pipelines play a critical role in preparing the datasets used to train models. Their work involves labeling and reviewing data according to detailed guidelines.
Some of the most common skills reported for this role include SEO copywriting, content marketing, and content creation. Data annotators are frequently employed in technology, staffing, and higher education, with work concentrated in Austin, New York City, and San Francisco.
Volen Vulkov, co-founder of Enhancv, points out that technology titles are becoming more and more popular.
“In reality, being an AI engineer can mean anything from working with high-volume APIs to consulting on business solutions around bots,” he says. “Personal branding and CPI advocacy are therefore central to advancing your IT career.”
He added that the rise of independent consultants, founders, and technology strategy and hybrid roles shows that while traditional IT job security is becoming increasingly unreliable, independence provides a new form of long-term security.
Burkoff says independent companies are building “portable” career assets in the background to enable independence from the “career capital” of traditional IT jobs.
“They place a high value on creating solutions, case studies, and knowledge pathways that enhance their reputation, validate their brand, and generate inbound work,” he explains.
Data center technicians are in high demand
Beyond AI development, the report highlights the continued demand for data center technicians to install, maintain, and troubleshoot servers and related hardware to ensure reliable operations.
Key skills include data center infrastructure, data center operations, and cabling. This role is most common in IT services and technology companies, with the highest recruitment rates in Washington, DC, Atlanta, and Columbus, Ohio.
In the context of large infrastructure projects, commissioning managers are responsible for testing and validating complex systems, including data centers, before they go into operation.
Key skills include electrical testing, piping and instrumentation drawings, and equipment testing. These roles are focused on engineering services and IT consulting, with hiring centered around Houston, Washington, DC, and Dallas.
The report also includes quantitative researchers and analysts who develop mathematical and statistical models to support investment and risk decisions. Common skills include algorithmic trading, statistical research, and backtesting. These roles span capital markets, technology, and research services and are most in demand in New York City, Chicago, and Boston.
Gohar said infrastructure and operations jobs such as cloud engineers, site reliability engineers, and platform engineers remain in high demand as back-end complexity increases with the advent of AI.
He explains that the nature of applications being developed with the help of AI is that they tend to be computationally intensive, costly, and unusable.
“It’s no surprise that employers are looking for people with the expertise to run their businesses properly, cost-effectively and reliably,” Gohar says. “These are all very foundational infrastructure and operational roles. You can’t scale your AI business without these teams.”
Prepare for the future of AI
Burkoff says that considering the “washout” of technical jobs with consulting and strategic roles (or so-called hybrid roles), IT careers cannot be solved by just a list of projects on a traditional resume.
“Narrative, business-centric storytelling is a game changer,” he says. “The value of technology solutions today is all about business impact, so this is what we encourage IT applicants to do for their projects and think narratively and map it onto their resumes.”
He encourages applicants to consider questions such as: How did this chatbot redesign improve first contact resolution? What did our work on model evaluation improve or reduce in our existing workflows?
Gohar said the growing need for AI is eroding the traditionally defined boundaries of IT at a fundamental level.
“Application programming is no longer the domain of software engineers, because software engineers need to be involved in data pipelines, experimentation, and AI,” he points out.
The tasks of infrastructure engineers are also changing themselves, increasing the need for cloud-native infrastructure that supports GPUs, automation, and scaling.
Even the tasks of data annotation engineers are becoming niche, increasing the need for domain knowledge, data quality, and ethics on the part of hiring organizations.
“From an HR perspective, AI is making career paths less linear and forcing all IT skills into new forms,” says Gohar.
