| Key Accountability | Key Responsibilities |
| MLOps Infrastructure and Deployment Enablement | Design, implement, and support reusable MLOps infrastructure, templates, and deployment patterns. Build and improve CI/CD workflows for automated testing, model packaging, artifact versioning, release approval, deployment, rollback, and promotion across environments. Establish repeatable training and retraining patterns, production-readiness standards, technical documentation, and operational runbooks. |
| Observability, Reliability, and Model Operations | Design and maintain monitoring for model performance, data and feature quality, drift, pipeline health, latency, failures, usage, and other operational indicators. Implement actionable alerts and diagnostic capabilities. Troubleshoot production issues across models, application code, data, pipelines, and platform components; conduct root-cause analysis and implement durable corrective actions. |
| Reusable Platform Patterns and Developer Experience | Create governed and scalable development and deployment patterns in partnership with Data Engineering, IT, Security, and platform teams. Improve reproducibility, testing, dependency management, access controls, secrets management, auditability, and maintainability. Develop reusable components, documentation, and self-service capabilities that make approved patterns easier for data science teams to adopt. |
| MLOps Maturity and Continuous Improvement | Evaluate emerging tools, approaches, and automation opportunities. Develop proofs of concept and reusable assets that improve deployment speed, reliability, governance, cost efficiency, and overall MLOps maturity. Recommend practical improvements based on measurable organizational needs. |
