Currently tracking 14 active AI roles, up 75% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $105k–$225k (avg $192k).
| Title | Stage | AI score |
|---|---|---|
| Quantitative Finance Analyst This role focuses on quantitative finance analysis within Bank of America's Global Risk Management division. It involves developing and implementing quantitative models, conducting market risk stress testing, and performing statistical analysis on large datasets. A significant part of the role includes designing features for data-centric AI solutions, contributing to the architecture and prototyping of data and AI-powered solutions, and evaluating data & AI tools through proof of concepts. The analyst will also be involved in data management, data architecture, and data platforms, ensuring high-quality data for quantitative modeling and evangelizing new data & AI solutions. | Data | 5 |
| Feature Lead - GenAI Team Feature Lead Data Engineer to build out data pipelines to source large volumes of structured (ex: KDB) & unstructured data (ex: Research documents, Term Sheets), classify, and store data to meet GenAI requirements. The role will design, develop, and engineer platform for high performance and scalability. |
| DataAgent |
| 5 |
| Ops Professional MKTS - Global Markets Shared Operations & Corporate Treasury Operations| Data, Business Intelligence and AI Lead This role focuses on developing and maintaining automated ETL pipelines, analyzing complex data sources using GenAI tools, designing data models for AI use cases, and driving continuous improvement through AI and automation within Global Markets Operations at Bank of America. The candidate will partner with business stakeholders to translate needs into technical solutions and create documentation, leveraging AI tools throughout the process. | Data | 5 |
| Software Engineer III-Python Software Engineer III-Python at Bank of America, focusing on developing and delivering complex software requirements, including AI/ML/GenAI lifecycle management, frameworks using MLFlow/KubeFlow, fine-tuning, inference, and API-based applications. Requires strong Python skills, DevOps experience, and experience with CI/CD practices. | Data | 5 |