A person walks around JPMorgan Chase headquarters in New York on February 17, 2026.
Zamek | View Press | Corbis News | Getty Images
Before artificial intelligence can take over Wall Street jobs, it first needs to be created.
AI-related posts in banking: JP Morgan Chase, citygroup and capital one The number of job openings jumped 49% this year compared to 2025, to 139,819, according to an analysis by corporate hiring data firm Draup provided exclusively to CNBC.
According to Draup, the fastest growing area is the skill set related to AI agents, which sift through data from public job postings and platforms like LinkedIn. For example, mentions of agent orchestration, the ability to design agents that work together on tasks, jumped 1,721% this year.
“This is probably the hottest skill on Wall Street,” Draup CEO Vijay Swaminathan said in an interview. “This is a huge opportunity. We need people who understand data and people who understand AI and where to put it.”
The job postings signal that Wall Street banks are moving beyond chatbots to the next phase of their AI strategies that will impact executives, employees and shareholders. To fulfill the promise of AI to improve productivity and automate repetitive tasks, banks are hurtling into a future filled with armies of agents handling a growing proportion of the workforce.
While the initial wave of AI adoption was dominated by engineers and data scientists who built models and adapted them to enterprise data, the boom is expanding to include those responsible for integrating AI directly into business lines.
Implementing AI within a financial institution often requires the collaboration of multiple specialized agents. For example, one person inspects raw data, another person analyzes documents, and a third person checks regulatory compliance.
Employees involved in this process, Swaminathan said, are often referred to as forward-deployed engineers and require a combination of technical capabilities and expertise in specific businesses and functions, from trading desks to back-office operations to human resources.
“There’s a lot of complexity in enterprises,” Swaminathan says. “These complexities are sometimes visible, but often hidden. Even simple processes take a long time to automate.”
For example, creating a team of agents to automate the approval of employee leave requests creates a web of edge cases and specific exemptions, he said.
Agent orchestration skills are especially important for forward-deployed engineers. That’s because the engineer’s job is to figure out which agents are needed, what each one does, and what technology to use, Draup CEO said. It also includes determining when human supervisors need to be involved, he said.
Agent technology stack
Other in-demand skills related to building AI include understanding the tools and techniques that enable agents to do their jobs.
According to Draup’s analysis, mentions of LangGraph, a framework for building multi-step workflows, increased by 679%, and mentions of LlamaIndex, which helps connect AI applications to data, increased by 291%. Mentions of search augmentation generation (RAG), a technology that feeds information from corporate databases to AI models, increased by 259%.
But beyond technical abilities, so-called soft skills are becoming increasingly important.
“Our analysis shows a new focus on soft skills such as problem-solving, creativity, the ability to ask difficult questions, and being proactive to better understand a process,” he said.
Other growth areas within AI include areas responsible for building guardrails around emerging systems, such as the demand for risk and control infrastructure.
According to Drup, mentions of “responsible AI” in job postings have increased by 657% this year, while references to AI governance and risk management have increased by 394% and 359%, respectively. Security teams also focus on preventing system vulnerabilities created by connecting third-party tools and external models.
Governance-related skills currently account for more than 16,000 references in Drup data, nearly double the approximately 8,400 references related to training, deploying, and running models.
“From a cybersecurity perspective, the focus is on ensuring that the third parties we use for these products are not fraudulent,” he said.
Roles related to generative AI and agents pay better than technical roles in other areas of the financial industry, with the median base salary for generative AI managers at about $190,000, Drapp said.
Despite higher salaries, fulfilling these specialized roles remains a challenge, Swaminathan said.
To fill this gap, he said, large banks are putting a lot of emphasis on internal reskilling programs to train existing developers and domain experts.
JPMorgan CEO Jamie Dimon spoke of “massive repositioning plans” as AI takes over more jobs.
“I think the more we prioritize soft skills with the right amount of technical skills, the more people will adapt and learn,” Swaminathan said. “This is a very exciting time for the right talent.”
