Responsibilities

The MLOps Engineer will develop and manage infrastructure for distributed model training using tools like SageMaker, Ray, and Kubernetes, deploy ML models utilizing containerization and orchestration, integrate ML workflows into CI/CD pipelines, create and maintain robust data and ML pipelines, implement experiment tracking and observability systems, and establish effective monitoring, logging, and governance practices for ML systems. They will also be responsible for creating and maintaining data architectures optimized for AI/ML and familiarizing themselves with RAG systems and agentic applications.