"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
- 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"
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