Principal ML Ops Engineer
Tasks
- Build auto scaling and request routing systems
- Collaborate with infrastructure platform and AI teams
- Design build and operate production ML inference infrastructure
- Develop scalable deployment pipelines
- Establish engineering best practices and platform architecture decisions
- Implement observability for inference and infrastructure costs
- Manage model registries and CI CD workflows
- Optimize GPU utilization memory and network performance
- Own ML system lifecycle from development to production operations
Perks/Benefits
- Exposure to cutting-edge technologies
- Fully remote
- Opportunity to own infrastructure
- Remote flexibility
- Technical leadership
Skills/Tech-stack
Autoscaling | Blue-Green Deployment | Blue/green | CI/CD | CUDA | Canary Deployment | Distributed Systems | Docker | Experiment tracking | GPU Utilization | GPU scheduling | Helm | Infrastructure as Code | Kubeflow | Kubernetes | Logging | MLOps | MLflow | Machine Learning | Memory Optimization | Metrics | Model Registry | Model Serving | Monitoring | NVIDIA Triton | Network Performance | Observability | Python | ROCm | TGI | Terraform | VLLM | “as-code”
Education
N/A
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