Senior MLOps Engineer

## About the Role Crunchyroll is hiring a Senior MLOps Engineer in Hyderabad to build and operate the machine learning infrastructure required to move models reliably from research into production. The role sits within the AI/ML organization and focuses on ML pipelines, model deployment, observability, automation, cloud infrastructure, and production ML lifecycle management. ## Responsibilities - Design, build, and maintain end-to-end ML infrastructure and production ML pipelines. - Develop CI/CD pipelines for automated and reliable ML model delivery. - Implement model registries, experiment tracking, versioning, and lifecycle management. - Build monitoring and observability systems for model drift, degradation, and production anomalies. - Partner with data scientists and ML engineers to productionize models. - Optimize ML training and inference workflows for scalability, performance, and cost. - Operate ML workloads using AWS SageMaker, Databricks, Kinesis, Lambda, EKS, and Docker. - Build reproducible training, validation, deployment, monitoring, and retraining workflows. - Integrate ML services with large-scale distributed systems. - Establish best practices for ML security, governance, compliance, testing, and automation. ## Requirements - 8+ years of experience in MLOps, ML infrastructure, or DevOps for AI/ML systems. - Strong Python and scripting skills. - Hands-on experience with MLflow, SageMaker, or Databricks ML. - Experience with Airflow or AWS Step Functions for workflow orchestration. - Strong CI/CD and automation experience using GitHub Actions, Terraform, CloudFormation, or similar tools. - Hands-on experience with Docker and Kubernetes/EKS. - Strong AWS experience, particularly with SageMaker, Lambda, S3, Kinesis, and Step Functions. - Understanding of ML model monitoring, observability, security, governance, and compliance. - Experience working closely with data scientists and ML engineering teams. ## Nice to Have - Experience with PyTorch, TensorFlow, or Scikit-learn production deployments. - Experience implementing model drift and data drift detection. - Experience with LLM production infrastructure. - Experience building highly automated ML platforms. - Experience with Infrastructure as Code at scale. - Knowledge of distributed systems and cloud-native architecture.