Machine Learning Operations (MLOps) Engineer
Core
Build and maintain robust pipelines for continuous integration, delivery, and monitoring of ML models to bridge the gap between model development and production deployment.
Role type
MLOps Engineer
Builds
End-to-end MLOps lifecycle, CI/CD pipelines, and infrastructure for training and serving ML models
Domain
Machine Learning Operations / Cloud Infrastructure
Deliverable
production ML models
Required skills
Python, Bash, Docker, Kubernetes, AWS/Azure/GCP, TensorFlow, PyTorch, scikit-learn, CI/CD principles, infrastructure as code
Responsibilities
Develop and automate the end-to-end MLOps lifecycle from data ingestion to model deployment and monitoring; Implement CI/CD pipelines for machine learning models; Build and manage infrastructure for training and serving ML models; Monitor model performance in production, identify drift, and implement strategies for retraining and redeployment; Collaborate with data scientists and software engineers to streamline model development and deployment processes; Ensure the reliability, scalability, and security of ML systems in production environments.
