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.