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 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 stateful engines, message brokers, and distributed caches onto Kubernetes platforms.
• Translate field-tested code and customer pain points into prioritized feature requirements for the core engineering team.
• Troubleshoot live networking, storage, and GPU failures while optimizing performance in production environments.
• Manage infrastructure as code using Terraform, Helm, and GitOps tools to automate deployment workflows.

What we're looking for

• 12+ years of related experience are required.
• A Bachelor's degree is preferred, though relevant experience may be considered in lieu of a degree.
• Experience must include operating GPU accelerated Kubernetes nodes and distributed AI orchestration tools like Ray or vLLM.
• Candidates must have hands-on experience with stateful workloads including Redis, Kafka, RabbitMQ, and various NoSQL databases.
• Proficiency is required in Infrastructure as Code and GitOps tools such as Terraform, Helm, and ArgoCD.
• Technical depth across multiple Kubernetes distributions like OpenShift, EKS, GKE, AKS, or Rancher is required.
• Experience must include deploying and scaling containerized data services and AI engines using Spark, Flink, or Airflow.
• Candidates must demonstrate the ability to translate field requirements into product specifications for core development teams.