Develop and maintain MLOps pipelines to automate the lifecycle of machine learning models.
Implement and optimize CI/CD pipelines specific to machine learning workflows.
Continuously monitor model performance in production, including logs, metrics, and alerts.
Design and apply strategies to detect and mitigate model performance degradation.
Ensure the security, governance, and compliance of data and models in production environments.
Collaborate with multidisciplinary teams to document and standardize MLOps-related processes.

Advanced experience in MLOps, including pipeline automation and integration.
Proficiency with containers (Docker, Kubernetes) and workflow orchestration. Knowledge of cloud providers (AWS) and their services applied to machine learning. Programming in Python for data operations and model workflows.
Experience implementing monitoring, logging, and observability for production models. Strong understanding of data security, governance, and compliance best practices.

Data Engineering Specialist – Data Architecture

Key skills