MLOps Engineer
Core
Build and maintain robust machine learning infrastructure to deploy, monitor, and continuously improve AI models from experimentation to production.
Role type
MLOps Engineer
Builds
Scalable cloud-based ML infrastructure and end-to-end ML deployment pipelines
Domain
Cloud computing, Machine Learning Operations, DevOps
Deliverable
production ML models
Required skills
Python, cloud platforms (Azure/AWS/GCP), containerization (Docker/Kubernetes), CI/CD pipelines, Infrastructure as Code, ML lifecycle tools (MLflow/Kubeflow/Airflow), model versioning and governance
Preferred skills
Machine learning frameworks, production deployment processes, troubleshooting, automation
Responsibilities
Design and maintain end-to-end ML deployment pipelines; Automate model training, testing, deployment, and monitoring; Build and manage scalable cloud-based ML infrastructure; Monitor model performance and ensure reliability; Implement CI/CD best practices; Manage model versioning, governance, and lifecycle processes
Seniority
Mid-level, hands-on IC
