Principal ML Ops Engineer
Tasks
- Build auto scaling and multi model serving architectures
- Collaborate with infrastructure platform and AI teams
- Design build and operate production ML inference infrastructure
- Develop scalable deployment pipelines
- Establish engineering best practices and architecture decisions
- Implement observability for inference and infrastructure health
- Manage model registries and CI CD workflows
- Optimize GPU utilization memory efficiency and network performance
- Own ML system lifecycle from development to production operations
Perks/Benefits
- Career growth opportunities
- Exposure to distributed systems and GPU computing
- Fully remote flexibility
- High ownership
- Meaningful technical impact
- Opportunity to build greenfield infrastructure
- Remote-first environment
- Technical leadership influence
Skills/Tech-stack
Auto Scaling | Blue-Green Deployment | Blue/green | CI/CD | CUDA | Canary rollout | Distributed Systems | Docker | Experiment tracking | GPU scheduling | Helm | Infrastructure as Code | Kubeflow | Kubernetes | Learning operations | MLOps | MLflow | Machine Learning | Machine Learning Operations | Model Registry | Model Serving | NVIDIA Triton | Observability | Performance optimization | Python | ROCm | Reliability Engineering | Scalability | TGI | Terraform | VLLM | “as-code”
Education
N/A
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