Senior Risk ML Engineer for Fraud & AML AI Workflows

tsys; Alpharetta (GA); On-site; USD 180,000 - 250,000; Full time

Job summary

tsys is seeking a hands-on Lead Risk Data Scientist & ML Engineer in Alpharetta, GA to own end-to-end fraud, AML, and risk model development and delivery. You will drive model performance, design AI-assisted analyst workflows, and ensure regulatory alignment, while leading a small, high-impact team.

You will partner across Risk, Compliance, and Technology to translate regulatory requirements into technical execution and scale deployment of detection capabilities.

Qualifications

• 7+ years in data science, machine learning or MLOps.
• Proven experience developing, deploying, and maintaining detection models in production.
• Hands-on experience with AI-assisted workflows, LLMs, and agentic frameworks.
• Experience in regulated financial services or fintech environments preferred.
• Exposure to SR 11-7 model risk management frameworks and regulatory interactions.

Responsibilities

• Own end-to-end delivery of detection models and AI-assisted workflows.
• Drive model performance through design, validation, production deployment and optimization.
• Define governance standards for model development, validation, and change management.
• Partner with Risk, Compliance, and Technology to align detection strategies.
• Lead cross-functional project teams and mentor data scientists and engineers.

Skills

Python

SQL

MLOps

LLMs

Agentic frameworks

Fraud risk knowledge

Regulatory awareness

Tools

Databricks

Snowflake

AWS SageMaker

GitHub Actions

CI/CD

Job description

tsys is seeking a hands-on Lead Risk Data Scientist & ML Engineer in Alpharetta, GA to own end-to-end fraud, AML, and risk model development and delivery. You will drive model performance, design AI-assisted analyst workflows, and ensure regulatory alignment, while leading a small, high-impact team.

You will partner across Risk, Compliance, and Technology to translate regulatory requirements into technical execution and scale deployment of detection capabilities.