Embedded engineering role working directly with enterprise clients and core product teams to architect, deploy, and troubleshoot production-grade VKS and hybrid Kubernetes platforms, migrate bare metal and legacy systems, and deploy/scale AI inferencing and data services. Responsibilities include field implementation, writing production code in customer environments, feeding field learnings into core engineering, and supporting GPU-accelerated model serving and distributed data stacks.

Principal-level Forward Deployed Engineer role responsible for field-to-engineering leadership across complex enterprise accounts. Responsibilities include architecting and validating vSphere/Kubernetes/VCF topologies, designing production LLM inferencing and distributed AI pipelines on GPU clusters, leading large-scale legacy-to-cloud-native migrations, developing production operators/CRDs, and influencing product roadmaps. Requires extensive experience with Kubernetes internals, operators, GitOps, GPU infrastructure, distributed stateful systems, and systems programming in Go/Python/Java.

Embedded engineering role within the Infrastructure Software Division focused on Kubernetes (VKS/vSphere), large-scale bare metal and legacy migrations, and production AI infrastructure (GPU-accelerated model serving, vector search, distributed data services). Responsible for end-to-end engagements, field implementations, prototyping, and feeding product requirements back to core engineering teams. Requires 12+ years related experience.

Hands-on engineering role embedded with enterprise client teams to lead end-to-end platform modernization, large-scale stateful workload migrations, VKS/vSphere Kubernetes deployments, and production AI/inference infrastructure. Requires writing production code in the field, troubleshooting networking/storage/GPU issues, and feeding field-driven requirements back into core product engineering.

Embedded engineering role working directly with enterprise client teams and core product engineering to design, deploy, and stabilize VKS/vSphere Kubernetes clusters, migrate bare metal and legacy platforms, and run production AI/inference workloads (GPU/vGPU/MIG). Responsibilities include hands-on coding, manifests, debugging production networking/storage/GPU issues, synthesizing field feedback into product features, and modernizing data/AI stacks (Ray, vLLM, Spark, Airflow, Trino, Milvus/Qdrant). 12+ years related experience required.