Forward Deployed Engineer (Staff)

What you'll do

• Write production code and build prototype integrations directly in customer environments to resolve technical hurdles.
• Architect, deploy, and maintain production-grade vSphere Kubernetes Service (VKS) clusters for 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 on virtualized GPU resources.
• Containerize and migrate stateful engines, message brokers, and distributed caches onto Kubernetes platforms.
• Identify recurring migration blockers and develop field fixes to accelerate software iteration cycles.
• Translate field-tested code and customer pain points into prioritized features for the core engineering team.
• Manage complex infrastructure deployments using Terraform, Helm, Kubernetes Operators, and GitOps workflows.

What we're looking for

• 12+ years of related experience required.
• Bachelor's degree preferred (or equivalent years of experience).
• Experience operating GPU accelerated Kubernetes nodes, model serving runtimes, vector databases, and distributed AI orchestration.
• Experience containerizing and operating high throughput/low latency platforms including Redis, Kafka, RabbitMQ, and Hadoop.
• Technical depth in deploying and scaling containerized data services, AI engines, and orchestrators like Spark, Flink, and Airflow.
• Proficiency with Infrastructure as Code and GitOps tools including Terraform, Helm, Kubernetes Operators, Cluster API, ArgoCD, and Flux.
• Experience with multiple enterprise Kubernetes distributions such as OpenShift, EKS, GKE, AKS, or Rancher.
• Ability to translate technical customer requirements into product specifications for core development teams.