Senior MLOps Engineer
Tasks
- Build containerized environments
- Deploy ML models to production
- Maintain cloud infrastructure
- Manage IAM RBAC policies
- Manage Kubernetes clusters
- Monitor model performance
- Operate ML experiment tracking and registry
- Own CI/CD pipelines
- Provision and schedule GPU resources
- Support workflow orchestration platforms
- Write Python tooling for ML infrastructure
Perks/Benefits
- Dental insurance
- Employee assistance program
- Extended long weekends
- Flexible hours
- Health insurance
- Holiday shutdown
- Learning and development budget
- Maternity leave
- Paid time off
- Parental leave top-up
- Unlimited personal days
- Vision insurance
Skills/Tech-stack
Argo | ArgoCD | Batch inference | CI/CD | CircleCI | Cloud platform | DDP | Distributed Training | Docker | Experiment tracking | GPU Computing | GPU drivers | GitHub | GitHub Actions | Google Cloud | Google Cloud Platform | Helm | IAM | Infrastructure as Code | Kubeflow | Kubernetes | ML experiment tracking | MLflow | Machine Learning | Machine learning lifecycle | Model Deployment | Nextflow | PyTorch | Python | RBAC | Ray | Realtime Inference | Seqera | Terraform | Vertex AI | VertexAI | WandB | Weights and Biases | “as-code”
Education
N/A
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