Staff Forward-Deployed Engineer: AI & Kubernetes

Socket.dev; Tennessee; Hybrid; USD 111,000 - 177,000; Full time

Job summary

Broadcom seeks a hands-on Forward Deployed Engineer (Staff) to work embedded with enterprise client teams on Kubernetes, platform modernization, and AI infrastructure.

You will lead end-to-end engagements, write production code in the field, build prototypes, and help shape platform capabilities across the ISG. The role emphasizes migration from bare metal to cloud-native, deployment of VKS clusters, and scalable AI inference workloads in real-world environments.

Qualifications

• Engineering mindset with core software/R&D experience.
• Proven track record in long-term customer engagements.
• Translate customer requirements into product features for development teams.
• Hands-on experience with GPU-accelerated Kubernetes and model serving runtimes.
• Experience with data services and distributed AI frameworks.

Responsibilities

• Act as embedded engineering liaison across ISG to reduce deployment friction.
• Identify migration blockers and shorten feature iteration cycles.
• Stay embedded with customer teams from discovery to go-live and stabilization.
• Write production code, build manifests, debug field issues in production environments.
• Architect and deploy VKS clusters across VMware Cloud Foundation and hybrid clouds.
• Lead migrations from bare metal to cloud-native Kubernetes targets.

Skills

Kubernetes

AI Infrastructure

Distributed Systems

Terraform

Field Troubleshooting

GPU Acceleration

Education

Bachelor's degree preferred

Tools

VMware vSphere

VMware Cloud Foundation

Job description

Broadcom seeks a hands-on Forward Deployed Engineer (Staff) to work embedded with enterprise client teams on Kubernetes, platform modernization, and AI infrastructure.

You will lead end-to-end engagements, write production code in the field, build prototypes, and help shape platform capabilities across the ISG. The role emphasizes migration from bare metal to cloud-native, deployment of VKS clusters, and scalable AI inference workloads in real-world environments.