"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"
