Software Engineer II - Machine Learning & Agentic AI Solutions Lead for AML Technology

The position is described below. If you want to apply, click the Apply Now button at the top or bottom of this page. After you click Apply Now and complete your application, you'll be invited to create a profile, which will let you see your application status and any communications. If you already have a profile with us, you can log in to check status. Need Help? If you have a disability and need assistance with the application, you can request a reasonable accommodation. Send an email to Accessibility (accommodation requests only; other inquiries won't receive a response). Regular or Temporary: Regular Language Fluency: English (Required) Work Shift: 1st shift (United States of America) Please review the following

: Deliver technically advanced solutions in support of Truist’s Anti-Money Laundering (AML) Technology initiatives, with a focus on Machine Learning and Generative AI use cases. Partner with application teams, product owners, and internal stakeholders to design, guide, and implement model-driven solutions. Provide subject matter expertise throughout the model lifecycle, from concept through governance, approval, and deployment. Essential Duties And

Following is a summary of the essential functions for this job. Other duties may be performed, both major and minor, which are not mentioned below. Specific activities may change from time to time. • Serve as a technical advisor across AML Technology teams for Machine Learning and Generative AI initiatives, supporting multiple use cases and applications. • Collaborate with application teams, vendors, and internal partners to design, develop, and deploy predictive and AI-driven models aligned with financial crime detection objectives. • Provide expertise in model development, integration, and deployment — including supporting teams in configuring solutions, performing analysis, and enabling scalable implementations. • Guide and influence best practices for Machine Learning and AI adoption within AML Technology, helping define standards, approaches, and reusable frameworks. • Lead and contribute to model lifecycle activities, including development, testing, validation support, implementation, and post-deployment monitoring. • Support development of required model documentation, including Model Definition Documents (MDD), and ensure alignment with internal standards and regulatory expectations. • Partner with application teams to navigate AI governance processes, including model review, validation, risk assessment, and approval workflows required for production deployment. • Build and support monitoring, performance evaluation, and controls for models and associated applications in production environments. • Ensure adherence to security, compliance, and enterprise technology standards across all implemented solutions. • Help solve complex technical and operational challenges related to model integration, data pipelines, and AI deployment in enterprise systems. • Act as a mentor and resource for teammates, providing guidance on Machine Learning techniques, financial crime use cases, and technical implementation strategies.