Bengaluru, India
Live, first seen by us 52 days agoDevelop 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.
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
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.
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
Bachelor's or master's degree in computer science, engineering, or a related field
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)
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- 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
- 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
- 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
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.
- 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
- 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
- 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
- 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
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New ML Engineer postings at the employers we follow are up 10% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
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
Education
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.
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.
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Bengaluru, India
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