Bank of America currently has 20 active AI-related job listings. The majority of these roles, 70%, are focused on agents, with post-training and data roles each comprising 10%. Engineering is the dominant function, with 18 listings. Recent hiring trends show a significant increase, with 14 new AI roles posted in the last 30 days, representing a 180% increase from the prior 30-day period.
Currently tracking 15 active AI roles, down 26% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $105k–$246k (avg $192k).
Bank of America currently has 23 active AI-related roles in our index. The most common open titles are: Artificial Intelligence Senior Security Engineer, Corporate Security Professional - Corporate Security Technology Innovation Manager, Data Scientist I, Data Scientist II, Director; Senior Architect. Most positions are in Engineering and Research.
Bank of America's active AI hiring is concentrated in: agents (57%), data (17%), serving infrastructure (13%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Bank of America is hiring AI talent in: United States (22 roles).
Job postings at Bank of America most frequently mention: System Design, AI Safety, Statistics, Large Language Models (LLMs), Generative AI.
In the past 30 days, Bank of America has posted 17 new AI-related roles. That is a +89% change versus the prior 30 days (9 → 17).
| Title | Stage | AI score |
|---|---|---|
| Global Transactional FX Product Manager Product Manager for Global Transactional FX, leading product strategy, commercialization, and performance management for automated FX conversion suite (Auto-FX and AutoConvert). Owns end-to-end product lifecycle including client value proposition, financial performance, go-to-market, client servicing, and analytics/model capabilities. Partners with Sales, Trading, Technology, Operations, and Risk/Compliance. Responsibilities include defining product strategies, P&L management, driving commercialization, establishing support models, owning risk and controls, managing data science and model lifecycle, performing quantitative analysis, driving experimentation, and leading the platform roadmap with Technology and Operations. | ShipPost-train | 5 |