{"id":3744143,"url":"https://alion.io/job/moodys-team-lead-mlopsai-devops","title":"Team Lead - MLOps/AI DevOps","company":{"id":58685,"name":"Moody's","domain":"moodys.com","url":"https://alion.io/company/moody-s","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AIOps","optional":false},{"name":"Amazon EKS","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"GitHub","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Grafana","optional":false},{"name":"IAM","optional":false},{"name":"Karpenter","optional":false},{"name":"KEDA","optional":false},{"name":"Kubernetes","optional":false},{"name":"Least Privilege","optional":false},{"name":"Machine Learning","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-11-04T10:46:30Z","employer_posted_date":null,"last_verified_at":"2026-11-04T10:46:30Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"Skills and Competencies :\n\n- 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.\n\n- 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.\n\n- 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.\n\n- Hands-on experience deploying and monitoring ML models in production, including ML/AI infrastructure, model observability, monitoring, and dashboarding using Grafana.\n\n- 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.\n\n- Strong system design and software engineering fundamentals, including first-principles engineering, software design patterns, scalability, reliability, performance, and problem-solving.\n\n- 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.\n\n- Deep expertise in artificial intelligence, with a track record of implementing advanced AI solutions to drive strategic transformation and operational efficiency.\n\n- Strong experience using AI tools to lead innovation initiatives.\n\n- Demonstrated leadership in managing AI-related risks, ensuring ethical governance, and fostering a culture of responsible AI adoption across the organization.\n\nEducation :\n\n- Bachelor's or master's degree in computer science, engineering, or a related field.\n\n- AWS certifications (e.g., AWS Certified Solutions Architect - Professional, AWS Certified Machine Learning - Specialty).\n\nResponsibilities :\n\n- 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.\n\n- 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.\n\n- 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.\n\n- 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.\n\n- 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.\n\n- 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.\n\n- Develop and enforce infrastructure and security best practices, including infrastructure provisioning with Terraform, model deployment, IAM, and security compliance.\n\n- Optimize cloud costs and AI solution performance, implementing effective workload and cluster autoscaling strategies using KEDA and Karpenter.\n\n- Build a high-performing engineering culture focused on teamwork, openness, innovation, mentoring, professional development, and continuous skill enhancement.\nSkills\nMLOps, AIOps, DevOps, AWS, CI/CD Pipeline, Cloud Services, Elastic Kubernetes Service, Performance Tuning, Github, Cloud Cost 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