Senior MLOps Engineer - Remote

Overview

The MLOps Engineer is responsible for designing, implementing, and maintaining scalable, reproducible, and auditable machine learning pipelines, bridging the gap between data science, engineering, and operations.

In Short

• Develop and maintain end-to-end ML pipelines using Kedro.
• Manage experiment tracking and model versioning with MLflow.
• Deploy models using ArgoCD and Kubernetes.
• Set up observability and performance monitoring with Grafana + Prometheus.
• Handle artifact and dependency management with JFrog Artifactory.
• Operate collaborative computing environments with JupyterHub.
• Automate the ML lifecycle using CI/CD pipelines integrated with AWS.
• Strong understanding of version control for code, data, and models.
• Knowledge of security and governance in data platforms.
• Excellent communication skills to interface between data and engineering teams.

Requirements

• Degree in Computer Engineering, Data Science, or related fields.
• Proven experience deploying ML solutions in production environments.

Benefits

• Opportunity to work with cutting-edge technology.
• Collaborative and innovative work environment.
• Professional growth and development opportunities.