Forward Deployed Engineer - Kubernetes
About The Position
Broadcom is seeking a hands-on Forward Deployed Engineer specializing in Kubernetes, platform modernization, large-scale stateful workload migration, and enterprise AI infrastructure. Embedded alongside enterprise client teams throughout engagements, this role supports the full lifecycle—from architecture and migration discovery through AI cluster setup, production rollout, and operational stabilization. The position is part of the Engineering Business Unit and works closely with product and core software teams as an extension of core engineering in the field. The engineer writes production code in customer environments, builds prototype integrations, troubleshoots live systems, and feeds field-tested solutions and customer needs back to engineering teams across the Infrastructure Software Division. The role aims to reduce customer adoption friction, shorten software iteration cycles, and strengthen the feedback loop between enterprise deployments and product development. The position covers production-grade vSphere Kubernetes Service (VKS) clusters across VMware Cloud Foundation and hybrid cloud infrastructure; migration of bare-metal, legacy, and data platforms; GPU/NPU-accelerated AI inferencing and related architectures; and containerization or migration of stateful data services, messaging systems, analytics platforms, and cloud-native data and AI stacks. It also involves integrating VKS with other Kubernetes distributions. A bachelor's degree is preferred, though relevant experience may be considered in lieu of a degree. At least 12 years of related experience is required.
Requirements
- At least 12 years of related experience.
- Deep alignment with product engineering workflows and experience working within or closely alongside core software or R&D organizations, rather than exclusively in IT or professional services.
- Proven experience staying engaged with customer technical leadership and developers through long-term, complex engineering projects.
- Ability to translate customer requirements and field workarounds into product specifications and feature requests for core development teams.
- Hands-on experience with GPU-accelerated Kubernetes nodes, model-serving runtimes, vector databases, and distributed AI orchestration.
- Hands-on experience containerizing and operating high-throughput or low-latency distributed systems and stateful workloads, including caching, in-memory grids, analytics, and messaging platforms.
- Deep technical knowledge of deploying, tuning, and scaling containerized data services, AI engines, and orchestrators.
- Technical proficiency with multiple Kubernetes distributions, such as OpenShift, EKS, GKE, AKS, or Rancher RKE/RKS.
- Advanced skills in Terraform, Helm, Kubernetes Operators (KPO), Cluster API (CAPI), and GitOps tools such as ArgoCD and Flux.
Nice To Haves
- Bachelor's degree preferred; relevant years of experience may be considered in lieu of a degree.
Responsibilities
- Serve as an embedded engineering liaison across the Infrastructure Software Division, collaborating with core software architects, product managers, and enterprise client developers to eliminate deployment friction.
- Identify recurring migration blockers and platform usability gaps, then build and test field fixes to accelerate software iteration.
- Remain engaged with customer technical teams from pre-migration discovery through go-live and operational stabilization.
- Implement and troubleshoot in production environments, including writing code, building manifests, debugging networking, storage, and GPU failures, and optimizing VKS performance.
- Translate field-tested code, architectural patterns, and customer pain points into engineering requirements and prioritized platform features with product managers and core engineers.
- Architect, deploy, and maintain production-grade VKS clusters across VCF and hybrid cloud infrastructure for general-purpose compute and accelerated GPU/NPU workloads.
- Lead migration strategies for enterprise platforms, large bare-metal environments, and legacy data stacks to VKS or other cloud-native Kubernetes targets.
- Deploy, tune, and scale AI inferencing workloads, RAG architectures, vector search, and model-serving frameworks on Kubernetes using virtualized GPU resources.
- Containerize, refactor, and migrate stateful engines, message brokers, distributed caches, and risk-calculation or analytics platforms to Kubernetes.
- Architect operator-driven, cloud-native deployments for data processing, orchestration, and distributed AI frameworks.
- Guide clients evaluating or operating VKS alongside other Kubernetes distributions, including OpenShift, EKS, GKE, AKS, and Rancher RKE/RKS.
Benefits
- Discretionary annual bonus eligibility.
- Competitive new-hire equity grant and opportunity for annual equity awards, subject to plan documents and award agreements.
- Medical, dental, and vision plans.
- 401(k) participation with company matching.
- Employee Stock Purchase Program (ESPP).
- Employee Assistance Program (EAP).
- Company-paid holidays.
- Paid sick leave and vacation time.
- Paid Family Leave and other leaves of absence in accordance with applicable laws.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
