ML Ops Infrastructure Engineer
Tasks
- Architect model deployment pipelines from staging to production
- Build A B testing infrastructure for model rollouts
- Build observability dashboards for model health
- Create build and test environments that mirror production
- Define model quality gates with research engineers
- Design CI CD pipelines for ML model development validation deployment
- Develop automated retraining pipelines for data changes and performance degradation
- Establish model versioning artifact management and rollback
- Implement production monitoring for model performance latency drift regression
- Optimize model serving infrastructure for latency throughput and cost
Perks/Benefits
- 401k match
- Conference and talks participation
- Flexible schedule
- Home office stipend
- Learning & education stipend
- Mental health support
- Paid US company holidays
- Paid parental leave
- Unlimited PTO
- Wellness stipend
Skills/Tech-stack
A/B | A/B Testing | Automated retraining | B testing | CI/CD | Canary Deployment | Data Versioning | Datadog | Docker | Drift Detection | Feature Stores | Feature evaluation | Grafana | Inference Server | Infrastructure as Code | Kubernetes | ML metadata | ML metadata management | Metadata Management | Model Deployment | Model Serving | Model Validation | Monitoring | NVIDIA Triton | NVIDIA Triton Inference | NVIDIA Triton Inference Server | ONNX Runtime | Observability | Progressive Delivery | Prometheus | Pulumi | Python | Quality Assurance | Regression Alerts | TensorRT | Terraform | Triton Inference Server | “as-code”
Education
N/A
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