Senior Risk ML Engineer: Fraud, AML & AI Workflows

Global Payments; Alpharetta (GA); On-site; USD 150,000 - 190,000; Full time

Job summary

Global Payments is seeking a hands-on Lead Risk Data Scientist & ML Engineer in Alpharetta, GA to own end-to-end detection models for fraud, AML, and credit risk. You will design, validate, deploy, and continuously optimize AI-powered workflows, driving model performance with production data and governance aligned with regulatory expectations.

You will partner with Risk, Compliance, and Technology to implement scalable data pipelines and agentic systems, mentoring a small but impact-driven team

Qualifications

• 7+ years in data science, machine learning or MLOps.
• Hands-on experience deploying detection models (fraud/AML/credit risk).
• Experience with AI-assisted workflows and agentic frameworks.
• Familiarity with regulated financial services and SR 11-7 is a plus.
• Proficiency in Python and SQL.

Responsibilities

• Own end-to-end ML model lifecycle: design, deployment, monitoring, and refinement.
• Lead AI-assisted analyst workflows and agent-based systems for risk operations.
• Define and refine detection strategies for fraud patterns and regulatory needs.
• Collaborate with Risk, Compliance, and Technology to align governance and change management.
• Provide mentorship and leadership to data scientists and engineers.

Skills

Python

SQL

MLOps

Databricks

Snowflake

SageMaker

Git

GitHub Actions

CI/CD

Model monitoring

Retraining pipelines

AWS Lambda

LLMs

Agentic frameworks

LangChain

Tools

Claude

GPT

AWS AgentCore

LangChain

Regulatory tooling

Job description

Global Payments is seeking a hands-on Lead Risk Data Scientist & ML Engineer in Alpharetta, GA to own end-to-end detection models for fraud, AML, and credit risk. You will design, validate, deploy, and continuously optimize AI-powered workflows, driving model performance with production data and governance aligned with regulatory expectations.

You will partner with Risk, Compliance, and Technology to implement scalable data pipelines and agentic systems, mentoring a small but impact-driven team