Forward Deployed Engineer (Staff)

What you'll do

• Write production code and build prototype integrations directly in client environments to resolve technical blockers.
• Architect, deploy, and maintain production-grade vSphere Kubernetes Service (VKS) clusters for general compute and GPU workloads.
• Lead the migration of large-scale bare metal environments and legacy data stacks to cloud-native Kubernetes targets.
• Deploy and scale AI inferencing workloads, RAG architectures, and vector search frameworks using virtualized GPU resources.
• Containerize and migrate heavy stateful engines, including message brokers like Kafka and distributed caches like Redis.
• Translate field-tested code and customer pain points into prioritized feature requirements for the core engineering team.
• Troubleshoot live networking, storage, and GPU failures in production environments to ensure successful platform adoption.
• Manage infrastructure as code using Terraform, Helm, and GitOps tools to automate deployment workflows.

What we're looking for

• 12+ years of related experience required.
• Bachelor's degree preferred.
• Experience in Kubernetes, platform modernization, large scale stateful workload migration, and enterprise AI infrastructure.
• Hands-on experience with GPU accelerated Kubernetes nodes, model serving runtimes, vector databases, and distributed AI orchestration.
• Expertise in containerizing and operating high throughput/low latency platforms including Redis, Kafka, RabbitMQ, and Spark.
• Advanced skills in Terraform, Helm, Kubernetes Operators, Cluster API, and GitOps tools like ArgoCD or Flux.
• Technical depth across multiple Kubernetes distributions such as OpenShift, EKS, GKE, AKS, or Rancher.
• Ability to translate technical customer requirements into product specifications for core development teams.