Position Summary :

Strong MLOps and production operations focus.

- Responsible for managing MLOps workflows, tools, and production support processes for ML solutions.

- Ensure day-to-day stability, reliability, and performance of ML models and pipelines.

- Manage model lifecycle controls, including versioning, lineage, reproducibility, monitoring, and governance.

- Develop incident handling, recovery, and escalation procedures for ML-related issues.

- Support data quality, lineage tracking, and governance practices across the ML lifecycle.

Responsibilities :

1. Design and Management :

- Responsible for designing, implementing, and managing MLOps workflows, tools, and operational processes for AIML solutions.

2. Operational Health :

- Oversee the day-to-day stability, reliability, and operational health of ML models and ML pipelines.

3. Lifecycle Operations :

- Manage model lifecycle operations, including model registration, versioning, deployment tracking, lineage, reproducibility, and governance.

4. Monitoring :

- Implement monitoring for data drift, concept drift, model performance degradation, inference quality, and service-level issues.

5. Incident Management :

- Develop and execute incident handling, recovery, rollback, and escalation plans for ML-related issues.