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

Truist•Atlanta, GA

5d•Onsite

About The Position

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.

Requirements

Bachelor’s Degree and four to seven years of experience or equivalent education and software engineering training or experience
In-depth knowledge in information systems and ability to identify, apply, and implement IT best practices
Understanding of key business processes and competitive strategies related to the IT function
Ability to plan and manage projects and solve complex problems by applying best practices
Ability to provide direction and mentor less experienced teammates.
Ability to interpret and convey complex, difficult, or sensitive information

Nice To Haves

Strong experience with Machine Learning techniques, model development, and deployment in enterprise environments.
Exposure to Generative AI technologies and frameworks (e.g., LLMs, prompt engineering, model orchestration).
Working knowledge of financial crimes / AML domain concepts (e.g., transaction monitoring, suspicious activity detection, fraud analytics).
Experience supporting or contributing to model governance, validation, or regulatory processes.
Strong foundation in data structures, algorithms, and software engineering principles.
Familiarity with open-source ML frameworks and tools (e.g., Python, TensorFlow, PyTorch, Scikit-learn).
Experience with cloud platforms such as AWS, Azure, or GCP, including ML/AI services.
Experience with data platforms, SQL, and data modeling.
Familiarity with CI/CD pipelines and tools such as Git, Jenkins, Maven.

Responsibilities

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.