Technology and Transformation - EAD- MLops - Consultant

Collaborate with data scientists, developers, and infrastructure teams to ensure seamless integration of machine learning models into production environments.

Develop and implement ML-Ops best practices to streamline the end-to-end machine learning lifecycle, from model training and testing to deployment and monitoring.

Design, build, and maintain scalable and reliable machine learning pipelines for data ingestion, preprocessing, feature engineering, model training, and deployment.

Implement and manage continuous integration and continuous deployment (CI/CD) processes for machine learning models.

Monitor the health and performance of deployed models, identifying and addressing issues related to data drift, model degradation, and performance bottlenecks.

Automate model retraining and deployment processes to ensure models remain up to date with changing data and requirements.

Collaborate with cross-functional teams to ensure data security, compliance, and privacy standards are met throughout the ML-Ops lifecycle.

Drive improvements in the machine learning infrastructure by evaluating new tools, technologies, and processes.

Participate in on-call rotations to address critical system issues as they arise.