Key Responsibilities
MLOps Lifecycle Ownership

Own the full ML lifecycle: data ingestion, model training, validation, deployment, monitoring, and retraining
Build and maintain robust model pipelines for computer vision and IRAS-based item recognition systems
Implement data and model monitoring (drift detection, performance degradation, alerting)
Refactor and productionize data science code into scalable, reusable services

Top Skills Details

1. Experience owning the MLOps lifecycle, from data monitoring to refactoring data science code to building a robust ML model lifecycle.
2. Python Engineering
3. CI/CD
4. Experience with MLOps-driven data science outcomes and handling ML Engineering horizontally, helping multiple products and initiatives.
5. Have strong knowledge of Machine Learning, MLOps, MLflow, Kubeflow, Python/R, SQL, Big Data, GCP, and Shell scripting.

Thanks & Regards,

Ash | [email protected]

+1 901 444 3152