Principal ML Ops Engineer
Tasks
- Build auto scaling systems multi model serving and request routing
- Collaborate with infrastructure platform and applied AI teams
- Design build and operate production ML inference infrastructure
- Develop scalable deployment pipelines
- Establish engineering best practices and contribute to platform architecture decisions
- Implement observability for inference latency throughput GPU usage and costs
- Manage model registries CI CD workflows and automation
- Optimize GPU utilization memory efficiency and network performance
- Own ML system lifecycle from development deployment to production operations
Perks/Benefits
- Flexible work schedule
- Fully remote work
- Ownership and influence
- Remote-first environment
- Startup growth opportunities
- Technical impact
Skills/Tech-stack
Auto Scaling | Blue-Green Deployment | Blue/green | CI/CD | CUDA | Canary Deployment | Distributed Systems | Docker | Experiment tracking | GPU scheduling | Helm | Kubeflow | Kubernetes | MLOps | MLflow | Machine Learning | Model Registry | NVIDIA Triton | Observability | Python | ROCm | TGI | Terraform | VLLM
Education
N/A
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