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About the Role
We are seeking a hands-on, highly engaged Forward Deployed Engineer (Staff) specializing in Kubernetes, platform modernization, large scale stateful workload migration, and enterprise AI infrastructure. You will be embedded directly alongside enterprise client teams throughout the entire end-to-end lifecycle of an engagement, from initial architecture and migration to live troubleshooting and production rollout. You will write production code in the field, build prototype integrations, and channel contributions back to our engineering teams to shape platform capabilities. This role operates as an extension of core engineering in the field, with the mission to reduce customer adoption friction, shorten software iteration cycles, and establish a high-bandwidth feedback loop between deployments and product development.
Responsibilities
- Act as an embedded engineering liaison across the Infrastructure Software Division, working directly with core software architects, product managers, and enterprise client developers to eliminate deployment friction.
- Identify recurring migration blockers and platform usability gaps, rapidly building and testing field fixes to shorten feature iteration cycles.
- Stay actively embedded with customer technical teams from pre-migration discovery through go-live and operational stabilization, ensuring successful platform adoption.
- Work shoulder-to-shoulder with client engineers in production environments to write code, build manifests, debug live networking/storage/GPU failures, and optimize VKS performance.
- Synthesize field-tested code, architectural patterns, and customer pain points directly into core engineering requirements and partner with product managers to translate contributions into prioritized platform features.
- Architect, deploy, and maintain production-grade vSphere Kubernetes Service (VKS) clusters across VMware Cloud Foundation (VCF) and hybrid cloud infrastructure, optimized for general purpose compute and accelerated GPU/NPU workloads.
- Lead technical migration strategies transitioning enterprise platforms, massive bare metal environments, and legacy data stacks over to VKS or cloud-native Kubernetes targets.
- Deploy, tune, and scale production AI inferencing workloads, RAG architectures, vector search, and model serving frameworks on Kubernetes using virtualized GPU resources.
- Containerize, refactor, and migrate heavy stateful engines, message brokers, distributed caches, and risk calculation/analytics platforms onto Kubernetes.
- Architect operator-driven, cloud-native deployments for data processing, orchestration, and distributed AI frameworks.
- Guide clients evaluating or operating VKS alongside competing distributions.
Requirements
- Deep alignment with product engineering workflows; experience functioning within or closely alongside core software/R&D divisions rather than pure IT or professional services.
- Proven track record of staying deeply engaged with customer technical leadership and developers across long-term, complex engineering projects.
- Demonstrated ability to translate raw technical customer requirements and field workarounds into clean product specifications and feature requests for core development teams.
- Hands-on experience operating GPU-accelerated Kubernetes nodes, model serving runtimes, vector databases, and distributed AI orchestration.
- Hands-on experience containerizing and operating high throughput/low latency platforms including Distributed Caching & In Memory Grids, Compute & Analytics Engines, and Messaging & Streaming.
- Deep technical knowledge of deploying, tuning, and scaling containerized data services, AI engines, and orchestrators.
- Technical depth across competing enterprise Kubernetes distributions (OpenShift, EKS, GKE, AKS, or Rancher RKE/RKS).
- Advanced skills in Terraform, Helm, Kubernetes Operators (KPO), Cluster API (CAPI), and GitOps (ArgoCD, Flux).
Skills
- Kubernetes
- Platform modernization
- Large scale stateful workload migration
- Enterprise AI infrastructure
- vSphere Kubernetes Service (VKS)
- VMware Cloud Foundation (VCF)
- Hybrid cloud infrastructure
- GPU/NPU workloads
- NVIDIA vGPU
- MIG
- Bare metal migration
- Legacy data stacks
- AI inferencing workloads
- RAG (Retrieval Augmented Generation)
- Vector search
- Model serving frameworks
- Apache Kafka
- RabbitMQ
- Redis
- Oracle Coherence
- Hazelcast
- Ray
- vLLM
- Apache Spark
- Apache Airflow
- Trino/Presto
- Flink
- Dask
- Cassandra/ScyllaDB
- Milvus/Qdrant
- Red Hat OpenShift
- Amazon EKS
- Google GKE
- Azure AKS
- Rancher RKE/RKS
- Terraform
- Helm
- Kubernetes Operators (KPO)
- Cluster API (CAPI)
- GitOps (ArgoCD, Flux)
- TGI
- Triton Inference Server
- Pgvector
- KubeRay
- Prefect
- Dagster
- Argo Workflows
- Elasticsearch / OpenSearch
- Pulsar
- Hadoop (HDFS/YARN)
Experience Level
- Staff
- 12+ years related experience required
Education Level
- Bachelor's degree preferred
- Relevant year's experience in lieu of a degree may be considered
Salary/Compensations
- USD 110,800.00 To USD 177,300.00
Benefits
- Discretionary annual bonus
- Competitive new hire equity grant
- Annual equity awards
- Medical plans
- Dental plans
- Vision plans
- 401(K) participation including company matching
- Employee Stock Purchase Program (ESPP)
- Employee Assistance Program (EAP)
- Company paid holidays
- Paid sick leave
- Vacation time
- Company follows all applicable laws for Paid Family Leave and other leaves of absence
About the Company
- Broadcom is proud to be an equal opportunity employer.
Equal Opportunity
- We will consider qualified applicants without regard to race, color, creed, religion, sex, sexual orientation, national origin, citizenship, disability status, medical condition, pregnancy, protected veteran status or any other characteristic protected by federal, state, or local law.
- We will also consider qualified applicants with arrest and conviction records consistent with local law.