Vacancy expired!
- Design, develop, deploy, and maintain production-grade scalable data transformation, machine learning and deep learning code, pipelines; manage data and model versioning, training, tuning, serving, experiment and evaluation tracking dashboards.
- Manage ETL and machine learning model lifecycle: develop, deploy, monitor, maintain, and update data and models in production.
- Build and maintain tools and infrastructure for data processing for AI/ML development initiatives.
- Develop ETL pipelines, tools and processing jobs for data cleansing, labeling and analysis.
- Experience deploying machine learning models into production environment.
- Strong DevOps, Data Engineering and ML background with Cloud platforms
- Experience in containerization and orchestration (such as Docker, Kubernetes)
- Experience building/operating systems for data extraction, ingestion and processing of large data sets
- Experience with MLOps tools such as MLFlow and Kubeflow
- Experience in Python scripting
- Experience with CI/CD
- Experience with ML training/retraining, Model Registry, ML model performance measurement using ML Ops open source frameworks
Vacancy expired!