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Tier-1 brand, popular ML role, metro location, and mid-level experience amplify competition.
Role requires specialized ML/MLOps skills and cloud experience, making cross-industry transferability limited.
Many mandatory MLOps, cloud, Kubernetes, and model governance skills create stringent technical filters.
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Manage end-to-end ML model lifecycle including versioning, deployment, rollback, A/B testing, and monitoring for production performance.
Develop, maintain, and optimize CI/CD pipelines and orchestration workflows for ML models using tools like GitLab CI, Airflow, or Argo Workflows.
Ensure ML pipeline governance, security, compliance, and cross-functional collaboration with data scientists, engineers, and architects.
Bachelor's or advanced degree in Computer Science, Software Engineering, or related field (or equivalent experience).
Experience designing and implementing cloud-based ML workflows on AWS (including AWS SageMaker).
Hands-on experience with MLOps frameworks such as MLflow, Kubeflow, Airflow and container orchestration tools like Docker and Kubernetes.
Work Experience Required: Not explicitly mentioned in the JD.
Demonstrated expertise in operationalizing ML models with strong focus on CI/CD, testing, monitoring, and governance.
Fluent in Python programming with familiarity in cloud ML services (AWS/Azure/GCP) and infrastructure-as-code tools.
Experience working in agile, cross-functional product development teams within digital marketing or data-driven environments.