Forward Deployed Engineer (Principal)

What you'll do

• Architect and validate production-grade vSphere Kubernetes Service (VKS) and VMware Cloud Foundation (VCF) topologies for mission-critical deployments.
• Design and deploy production systems for real-time LLM inferencing, RAG pipelines, and distributed AI runtime frameworks.
• Lead end-to-end technical migration strategies to move legacy bare metal systems and stateful engines to cloud-native Kubernetes targets.
• Write production operators, CRDs, and prototype integrations in the field to unblock strategic accounts and upstream them into core codebases.
• Translate complex customer pain points and field workarounds into concrete product requirements for the engineering roadmap.
• Establish technical patterns for containerizing and orchestrating distributed systems like Kafka, vector databases, and high-performance data engines.
• Define cross-platform integration strategies for clients operating in multi-cloud environments across competing distributions like OpenShift, EKS, and GKE.
• Mentor other engineers and establish best-in-class deployment standards, GitOps frameworks, and CAPI patterns across the organization.

What we're looking for

• Must have 10+ years of software engineering experience.
• Must have 5+ years of experience driving infrastructure, distributed systems, AI platforms, or Kubernetes architectures at scale.
• Alternatively, must have 17+ years of related experience if a degree is not held.
• Bachelor's degree preferred.
• Mastery of Kubernetes internals, CRDs, Operators, Cluster API (CAPI), and GitOps architecture.
• Experience architecting AI inferencing pipelines, virtualized GPU environments, and distributed inference topologies.
• Proficiency in developing systems-level infrastructure or automation frameworks using Go, Python, or Java.
• Proven ability to lead large-scale migrations from legacy bare metal or mainframe systems to cloud-native Kubernetes targets.