Principal ML Ops Engineer
Tasks
- Automate model deployments
- Build and operate model serving systems
- Build autoscaling systems
- Collaborate with infrastructure and platform teams
- Design production ML inference infrastructure
- Develop deployment pipelines
- Establish engineering best practices
- Implement Blue-Green deployments
- Implement CI/CD workflows
- Implement canary rollouts
- Implement intelligent request routing
- Implement observability for inference
- Maintain multi model serving architectures
- Manage model registries
- Monitor latency throughput and costs
- Optimize GPU utilization and memory efficiency
- Optimize model artifact storage
- Optimize network performance
- Own ML system lifecycle
Perks/Benefits
Skills/Tech-stack
Blue-Green Deployment | Blue/green | CI/CD | CUDA | CUDA optimization | Canary Deployment | Containerization | Distributed Systems | Experiment Management | Experiment tracking | GPU scheduling | Helm | Kubeflow | Kubernetes | MLOps | MLflow | Model Registry | Model Serving | Multi-tenancy | NVIDIA Triton | Network Performance | Network performance optimization | Observability | Performance optimization | Python | ROCm | ROCm Optimization | Reliability Engineering | Scalability | TGI | Terraform | VLLM
Education
N/A
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