Principal ML Ops Engineer
Tasks
- Automate reproducible model deployments
- Build and maintain autoscaling systems
- Design build and operate production ML inference infrastructure
- Develop scalable deployment pipelines
- Implement blue green deployments and canary rollouts
- Implement multi model serving architectures
- Manage model registries and CI CD workflows
- Monitor inference latency throughput and operational health
- Optimize GPU utilization memory efficiency and network performance
- Own ML system lifecycle from development through production operations
- Set up intelligent request routing
Perks/Benefits
- Fully remote
- High ownership
- Remote-first collaboration
- Startup growth opportunities
- Work flexibility
Skills/Tech-stack
Autoscaling | CI/CD | CUDA | Containerization | Distributed Systems | Experiment Management | Experiment tracking | GPU scheduling | Helm | Infrastructure as Code | Kubeflow | Kubernetes | MLflow | Memory Optimization | Model Registry | Network Performance | Network performance optimization | Observability | Performance optimization | Python | ROCm | Terraform | Text Generation | Text generation inference | Triton | VLLM | “as-code”
Education
N/A
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