MLOps Engineer role
Core
Design, implement, and optimize end-to-end MLOps pipelines for batch and real-time machine learning algorithms, ensuring scalability and robustness in production.
Role type
MLOps Engineer
Builds
Production ML models and supporting infrastructure
Domain
Machine Learning Operations / Cloud Infrastructure
Deliverable
production ML models
Required skills
Python, TensorFlow, PyTorch, scikit-learn, Kubeflow, Seldon, MLFlow, Docker, Kubernetes, GCP, Azure, CI/CD, ETL, feature engineering, Prometheus, Grafana
Preferred skills
Experience with Seldon, MLFlow, Prometheus, Grafana
Technologies
Kubeflow, Seldon, MLFlow, Docker, Kubernetes, GCP, Azure, Prometheus, Grafana
Responsibilities
Develop and deploy microservices-based solutions for batch and real-time algorithms; Design and optimize MLOps pipelines; Collaborate with Data Scientists to enhance model development; Monitor and troubleshoot ML model performance and infrastructure issues; Support ML software infrastructure including CI/CD and security; Optimize cloud resource allocation and costs.
Seniority
Mid-Senior, hands-on IC