Are you excited by the challenge of building intelligent, autonomous systems that deliver real-world impact? The Commerce Risk Analytics team at Microsoft is looking for a machine learning engineer to design, develop, and continuously improve a multi-agent AI Risk system that enables autonomous risk transaction review and decision-making. You’ll be working at the intersection of large language models (LLMs), applied machine learning, and platform integration—building solutions that power scenarios from fraud prevention to customer support automation.

We’re hiring across several focus areas, and your role will align with your expertise and interests:

• Autonomous Agent Development for Risk Decision – building LLM-based decision-making agents and advancing a multi-agent risk system

• AI Integration & MLOps – enabling scalable infrastructure, data pipelines, and operational excellence

• Quality & Integration – focusing on agent behavior testing, UI/UX integration, and platform reliability

As a machine learning engineer, you will:

• Design and optimize multi-agent AI architectures that enable autonomous risk assessment and decision-making at scale, leveraging agent collaboration to reduce huma manual effort and minimize false positive rates.

• Imlement agentic ML infrastructure that automates the full model development lifecycle enabling continuous learning, adaptive optimization, and scalable risk decisioning.

• Build and evolve AI-driven solutions that improve the accuracy, speed, and adaptability of fraud detection across a wide range of Commerce scenarios.

• Develop infrastructure and MLOps pipelines to support continuous training, deployment, and monitoring

• Conduct rigorous behavior testing and validation, focusing on performance, safety, and real-world edge cases

• Integrate AI agents with Azure AI services, Microsoft Graph API, Power Platform, and internal systems

• Apply best practices in security, compliance, and privacy to all aspects of agent development

• Partner closely with product managers, engineers, and data scientists to translate complex business challenges into scalable technical solutions, ensuring alignment and impact across stakeholders