AI Infrastructure Engineer III
USD 152K-273K (estimate) Senior-level Full Time
Tasks
- Automate AI infrastructure provisioning with Infrastructure as Code
- Build CI/CD pipelines for ML workloads
- Build scalable highly available model serving infrastructure
- Build self service platforms for data scientists and ML engineers
- Collaborate with data science teams to improve platform usability performance and reliability
- Deploy and manage NVIDIA GPU Operator and GPU enabled Kubernetes environments
- Deploy and operate Kubeflow and MLflow and KServe
- Design and operate GPU clusters for AI workloads
- Design deploy and operate enterprise AI ML platforms
- Design infrastructure for model training experimentation feature engineering and inference
- Implement monitoring and observability for GPU utilization model serving training and inference latency
- Optimize GPU scheduling utilization sharing autoscaling and resource allocation
- Optimize distributed GPU training performance
- Provision and operate distributed training workloads
- Troubleshoot AI infrastructure performance bottlenecks
Perks/Benefits
Skills/Tech-stack
AWS | Ansible | Autoscaling | Azure | Bash | CI/CD | CUDA | CUDA fundamentals | Distributed Training | Elasticsearch | GCP | GPU Operator | GPU Utilization | GPU scheduling | GitOps | Go | Grafana | Helm | Inference Server | Infrastructure as Code | KServe | Kubeflow | Kubernetes | MLflow | Model Serving | Monitoring | NVIDIA GPU | OCI | Observability | OpenSearch | OpenTelemetry | Prometheus | Python | Ray | Ray Serve | Terraform | Triton Inference | Triton Inference Server | “as-code”
Education
N/A
Roles
AI | AI Infrastructure Engineer | Engineer | Infrastructure Engineer | Platform | Platform Engineer
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