• Enable enterprise customers to operationalize AI workloads by deploying and optimizing model-serving platforms (e.g., NVIDIA Triton, vLLM, KServe) within Rackspace’s Private Cloud and Hybrid environments. • Package and deploy ML/LLM models on Triton, vLLM, or KServe within Kubernetes clusters. • Tune performance for latency and throughput SLAs. • Work with VMware VCF9, NSX-T, and vSAN ESA to ensure GPU resource allocation and multi-tenancy. • Implement RBAC, encryption, and compliance controls for sovereign/private cloud customers. • Integrate models with Rackspace’s Unified Inference API and API Gateway for multi-tenant routing. • Support RAG and agentic workflows by connecting to vector databases and context stores. • Configure telemetry for GPU utilization, request tracing, and error monitoring.