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.- Robust 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. (ref:hirist.tech)

📌 Moody's - Team Lead - MLOps/AI DevOps (Bengaluru)
🏢 Moody's Shared Services India Private
📍 Bengaluru