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 DevOps and cloud infrastructure engineering experience, with a DevOps-first mindset and extensive hands-on experience supporting, deploying, and managing large-scale machine learning (ML) and generative AI workloads
• Strong expertise in AWS services as the primary cloud platform, with exposure to additional cloud platforms preferred, and experience designing highly scalable ML solutions, web applications, and high-frequency APIs
• Proficiency in infrastructure-as-code, CI/CD, containerization, and orchestration, including Terraform, CloudFormation concepts, GitHub Actions, and Amazon EKS, with KEDA and Karpenter for workload and cluster autoscaling
• Hands-on experience deploying and monitoring ML models in production, including ML/AI infrastructure, model observability, monitoring, and dashboarding using Grafana
• Strong knowledge of secure cloud and ML/AI infrastructure, including IAM, least-privilege access, cloud security controls, and proven experience optimizing cloud costs at scale
• Strong system design and software engineering fundamentals, including first-principles engineering, software design patterns, scalability, reliability, performance, and problem-solving
• Proven hands-on leadership experience managing senior DevOps and MLOps engineers, delivering complex technical projects, and demonstrating strong communication, leadership, mentoring, and problem-solving skills
• Deep expertise in artificial intelligence, with a track record of implementing advanced AI solutions to drive strategic transformation and operational efficiency. Strong experience using AI tools to lead innovation initiatives. Demonstrated leadership in managing AI-related risks, ensuring ethical governance, and fostering a culture of responsible AI adoption across the organization

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)

Responsibilities

The Team Lead drives secure, scalable, and cost-efficient cloud and ML infrastructure while leading high-performing engineering teams aligned to Moody's strategic AI initiatives.

• Lead and manage senior DevOps, MLOps, and cloud system engineers, aligning the team with strategic AI initiatives while remaining hands-on with architecture, code reviews, and critical implementations
• Own end-to-end DevOps across the product suite, from design and development through deployment, applying strong practices in security, CI/CD, first principles, and software patterns
• Design and oversee secure, scalable, high-performance, and cost-efficient AWS cloud and ML infrastructure, including EKS-based platforms supporting ML workloads, web applications, and high-frequency APIs
• Build and maintain robust CI/CD pipelines using GitHub Actions for application and model delivery, while driving operational improvements through automation and infrastructure-as-code
• Partner with data scientists and engineers to optimize ML model deployment, monitoring, performance, and lifecycle management, and establish infrastructure and model observability standards using Grafana
• Develop and enforce infrastructure and security best practices, including infrastructure provisioning with Terraform, model deployment, IAM, and security compliance
• Optimize cloud costs and AI solution performance, implementing effective workload and cluster autoscaling strategies using KEDA and Karpenter
• Build a high-performing engineering culture focused on teamwork, openness, innovation, mentoring, professional development, and continuous skill enhancement.

About the Team

Moody’s Ratings’ AI Analytics group develops advanced machine learning (ML) and generative AI solutions for high-profile data science initiatives. The successful candidate will guide teams in building robust, scalable, and secure solutions on AWS. The successful candidate will strive to meet Moody’s values: openness, diversity, inclusivity, respect, and a willingness to learn.

Moody’s is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, sexual orientation, gender expression, gender identity or any other characteristic protected by law.

Candidates for Moody's Corporation may be asked to disclose securities holdings pursuant to Moody’s Policy for Securities Trading and the requirements of the position. Employment is contingent upon compliance with the Policy, including remediation of positions in those holdings as necessary.

For more information on the Securities Trading Program, please refer to the Securities Trading Policy on Moody’s Compliance Expo page

Please note: STP categories are assigned by the hiring teams and are subject to change over the course of an employee’s tenure with Moody’s.