Principal ML Ops Engineer
Tasks
- Design build production ML inference infrastructure
- Develop scalable deployment pipelines
- Establish engineering best practices and platform architecture
- Implement autoscaling and multi model serving
- Implement observability for inference systems
- Manage model registries and CI CD workflows
- Optimize GPU utilization and memory efficiency
- Own ML system lifecycle operations
Perks/Benefits
- Fully remote work
- Growth opportunities
- High ownership and influence
- Remote-first flexibility
- Technical impact
Skills/Tech-stack
Blue-Green Deployment | Blue/green | CI/CD | CUDA | CUDA optimization | Canary Deployment | Distributed Systems | Docker | Experiment tracking | GPU Utilization | GPU Utilization Optimization | GPU scheduling | Helm | Infrastructure as Code | Kubeflow | Kubernetes | MLOps | MLflow | Model Registry | NVIDIA Triton | Network Performance | Network performance optimization | Performance optimization | Python | ROCm | ROCm Optimization | Request Routing | TGI | Terraform | Utilization Optimization | VLLM | “as-code”
Education
N/A
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