AVP Mgr-Machine Learning at Moody's

At Moody's, we unite the brightest minds to turn today’s risks into tomorrow’s opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they are—with the freedom to exchange ideas, think innovatively, and listen to each other and customers in meaningful ways. Moody’s is transforming how the world sees risk. As a global leader in ratings and integrated risk assessment, we’re advancing AI to move from insight to action—enabling intelligence that not only understands complexity but responds to it. We decode risk to unlock opportunity, helping our clients navigate uncertainty with clarity, speed, and confidence.

If you are excited about this opportunity but do not meet every single requirement, please apply! You still may be a great fit for this role or other open roles. We are seeking candidates who model our values: invest in every relationship, lead with curiosity, champion diverse perspectives, turn inputs into actions, and uphold trust through integrity.

Skills And Competencies

• 7+ years of experience in deploying and managing large-scale machine learning (ML) and generative AI applications.
• Strong expertise in AWS services and proficiency with infrastructure-as-code tools.
• Solid familiarity with CI/CD pipelines, containerization, and orchestration technologies.
• In-depth knowledge of ML/AI infrastructure and adherence to security best practices.
• Proven track record of managing teams and delivering complex technical projects.
• Excellent communication, leadership, and problem-solving skills.

Education

• Bachelor’s or master’s degree in computer science, engineering, or a related field.
• AWS certifications (e.g., AWS Certified Solutions Architect – Professional, AWS Certified Machine Learning – Specialty).

Roles And Responsibilities

• Lead and manage a team of MLOps engineers and cloud system engineers, ensuring alignment with strategic AI initiatives.
• Oversee the design and implementation of secure, scalable, and cost-efficient cloud and ML infrastructure on AWS.
• Collaborate with data scientists and engineers to optimize model deployment, monitoring, and lifecycle management.