\"Key Responsibilities:
•    Build and maintain ML pipelines for training, validation, deployment, and monitoring
•    Implement CI/CD for ML artifacts, including data versioning and model registries
•    Monitor model performance and drift; trigger retraining workflows as needed
•    Manage infrastructure (cloud, containerized, on-prem) for high-availability AI services
•    Collaborate with data, ML, and ops teams to deliver frictionless MLOps lifecycle
Required Skills:
•    Strong knowledge of MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI)
•    Proficiency in Python, Bash, Docker, and Kubernetes
•    Familiarity with cloud infra (AWS/GCP/Azure) and IaC (Terraform, CloudFormation)
•    Experience in model monitoring, logging, and alerting
•    Bonus: experience in BPS / regulated domains with compliance-aware deployment\"