MLOps engineer
Core
Design and implement cloud-based MLOps pipelines to deploy, maintain, and monitor machine learning models in production.
Role type
MLOps engineer
Builds
CI/CD pipelines, containerized ML models, and automated testing frameworks for data science models
Domain
Cloud infrastructure and Machine Learning Operations
Deliverable
production ML models
Required skills
Cloud platform expertise (AWS, Azure, GCP), MLOps frameworks (Kubeflow, MLFlow, Airflow), containerization (Docker, Kubernetes, OpenShift), programming (Python, Go, Ruby, Bash), Linux, ML libraries (scikit-learn, Keras, PyTorch, Tensorflow), test automation
Preferred skills
Associate Cloud Certification
Technologies
AWS, Azure, GCP, GitLab CI, GitHub Actions, Circle CI, Airflow, Kubeflow, MLFlow, DataRobot, Docker, Kubernetes, OpenShift, Python, Go, Ruby, Bash, Linux, scikit-learn, Keras, PyTorch, Tensorflow
Responsibilities
Design and implement cloud solutions for MLOps; build CI/CD pipelines using orchestration tools; review data science models and optimize code; handle containerization, deployment, versioning, and quality monitoring; automate model testing and validation; document processes and collaborate with data scientists and engineers
Seniority
Mid-level to Senior, hands-on IC
