Principal ML Ops Engineer
Tasks
- Automate reproducible model deployments
- Build and maintain autoscaling systems
- Collaborate with infrastructure platform and AI teams
- Design build and operate production ML inference infrastructure
- Develop scalable deployment pipelines
- Establish engineering best practices for ML platform architecture
- Implement observability for inference systems
- Manage model registries and CI CD workflows
- Optimize GPU utilization and memory efficiency
- Own ML system lifecycle from development to production operations
Perks/Benefits
Skills/Tech-stack
Auto Scaling | Blue-Green Deployment | Blue/green | CI/CD | CUDA | Canary Deployment | Distributed Systems | Docker | Experiment tracking | GPU scheduling | Helm | Infrastructure as Code | KubeAI | Kubeflow | Kubernetes | MLOps | MLflow | Machine Learning | Model Registry | Multi Tenant Inference | Multi-tenant | NVIDIA Triton | Network Performance | Observability | Performance optimization | Python | ROCm | Reliability Engineering | Request Routing | Scalability | TGI | Terraform | VLLM | “as-code”
Education
N/A
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