Infrastructure Engineer

• Design, deploy, and operate low-latency streaming inference systems on AWS for LLM, ASR, TTS, and multimodal models
• Optimize time to first token/audio, p95/p99 end-to-end latency, real-time factor, throughput, concurrency, GPU utilization, and cost per stream
• Support and evolve the research team’s AWS-based training environment
• Provide reproducible containers, GPU job scheduling, distributed job execution, checkpoint and resume capabilities, experiment tracking, model and data artifact access, and researcher self-service
• Build model and artifact registries, lineage and versioning, automated evaluation gates, deployment pipelines, shadow and canary releases, rollback workflows, runtime and configuration management, and production observability
• Partner with researchers and device and embedded teams to build model optimization, packaging, validation, and deployment workflows for resource-constrained edge targets
• Support model export and compilation, post-training quantization, and runtime integration
• Own capacity planning, production readiness, incident response, disaster recovery, and secure AI platform operation
• Apply least-privilege IAM, KMS encryption, private networking, and secrets management
• Take ownership of important initiatives and outcomes, drive business results, influence decisions, and partner with high-performing team members

Infrastructure Engineer jobs in Kansas

AI Infrastructure Engineer