AI Infrastructure Engineer vs AI Knowledge Systems Engineer

A AI Infrastructure Engineer Only

• Kubernetes orchestration and operator design for GPU workloads
• GPU cluster management including multi-tenancy, scheduling (e.g., Slurm, Kubernetes device plugins), and utilization monitoring
• ML model serving architectures (batch, real-time, streaming inference)
• Infrastructure as Code (Terraform, Pulumi) for reproducible AI environments
• Distributed training orchestration (PyTorch FSDP, DeepSpeed, Megatron-LM)
• Container optimization for ML - CUDA-aware images, layer caching, artifact management
• CI/CD pipelines for ML models and data (MLflow, DVC, ZenML, GitHub Actions)
• Observability and monitoring for ML systems (Prometheus, Grafana, custom latency/error dashboards)