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.
Requirements
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.
Tech Stack
AWS
Cloud
Docker
Kubernetes
Python
Benefits
Freedom to work from anywhere
Flexible working hours
Education assistance
Proprietary career development tool
Internal guilds and study/interest groups
Health insurance
Dental insurance
Discounted medication purchase partnership
24/7 telemedicine
Free online therapy
Wellhub
Extended maternity leave
Extended paternity leave
CAZ – Zuppers Support Center
Meal and grocery vouchers
Life insurance
Transport allowance
Home office allowance
Childcare allowance
Phone plan allowance
Profit-sharing (Participation in Profits and Results)