"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"