Senior MLops Engineer
Tasks
- Build and maintain scalable secure MLOps infrastructure
- Collaborate on end to end ML systems from experimentation to monitoring
- Create documentation and internal tooling for self service ML infrastructure
- Deploy and monitor machine learning models in production
- Ensure governance traceability and compliance of ML assets
- Implement automated testing for ML systems
- Improve observability and scalability
- Run retraining and model versioning pipelines
- Set up performance monitoring alerting and drift detection
Perks/Benefits
- N/A
Skills/Tech-stack
Alerting | Apache Airflow | CI/CD | Cloud platform | Data Lineage | Docker | Drift Detection | Feature Store | GitHub Actions | Google Cloud | Google Cloud Platform | Grafana | Infrastructure as Code | Kubeflow | Kubernetes | Machine Learning | Model Drift | Model Drift Detection | Model Registry | Monitoring | Prometheus | PyTorch | Python | SQL | Scikit-learn | TensorFlow | Terraform | Vertex AI | “as-code”
Education
N/A
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