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About the Role
Serve as an elite technical authority, enterprise strategist, and field to engineering leader across the Infrastructure Software Division (ISG). Embed with large, complex enterprise accounts during critical platform modernizations, bare metal re-platforming, large-scale stateful workload migrations, and mission-critical AI inferencing deployments. Operate at the intersection of enterprise architecture, production code, and long-term product strategy. Tackle systemic architecture blockers, author reference implementations, and influence the technical roadmap of core infrastructure and AI platform software. Eliminate enterprise adoption friction at scale, codify repeatable deployment patterns for modern workloads, and establish a feedback loop turning field innovations into core software capabilities.
Responsibilities
- Act as the primary technical authority bridging core engineering, product management executive leadership, and C-level enterprise customer architects.
- Architect, scale, and validate production-grade vSphere Kubernetes Service (VKS) and VMware Cloud Foundation (VCF) topologies for high-consequence, mission-critical, and highly regulated deployments.
- Design and deploy production systems for real-time LLM inferencing, Retrieval Augmented Generation (RAG) pipelines, and distributed AI runtime frameworks across high-density GPU clusters.
- Lead end-to-end technical migration strategies transitioning legacy bare-metal systems, risk calculation platforms, heavy stateful engines, and fragmented AI workloads to unified cloud-native Kubernetes targets.
- Directly engineer high-value prototype integrations, write production operators/CRDs in the field to unblock strategic accounts, and upstream contributions into core ISG codebases.
- Synthesize complex customer pain points, field workarounds, and architectural patterns into concrete product requirements. Partner with VP/Director-level Product Managers to prioritize features that shorten software iteration cycles.
- Set technical patterns for containerizing, tuning, and orchestrating heavy distributed systems, including streaming platforms, vector databases, in-memory grids, and advanced AI data/compute engines.
- Define cross-platform integration strategies for clients operating mixed environments across competing distributions.
- Establish best-in-class deployment standards, GitOps frameworks, and CAPI/Operator patterns across the broader FDE organization; mentor Senior and Staff FDEs to elevate team-wide execution capabilities.
Requirements
- 10+ years of software engineering experience with 5+ years driving high-consequence infrastructure, distributed systems, AI platforms, or Kubernetes architectures at scale.
- Demonstrated alignment with core R&D/Product Engineering organizations.
- Proven ability to command technical credibility with C-suite stakeholders while remaining deeply hands-on in production code with staff-level customer engineers.
- Authority-level mastery of Kubernetes internal mechanics, Custom Resource Definitions (CRDs), Kubernetes Operators, Cluster API (CAPI), and GitOps architecture.
- Extensive experience architecting high-throughput AI inferencing pipelines, virtualized GPU environments, model serving frameworks, and distributed inference topologies.
- Proven track record architecting and running high-throughput, low-latency stateful workloads on Kubernetes.
- Deep experience untangling complex enterprise monoliths, mainframe/bare-metal environments, and siloed AI hardware deployments to construct seamless zero-downtime migration pathways.
- Hands-on experience developing in Go, Python, or Java for systems-level infrastructure, Kubernetes controllers, custom AI wrappers, or automation frameworks.
- Comprehensive technical fluency across competing enterprise distributions and their underlying networking and storage abstractions.
Skills
- Kubernetes
- Custom Resource Definitions (CRDs)
- Kubernetes Operators
- Cluster API (CAPI)
- GitOps (ArgoCD, Flux)
- AI Inferencing Pipelines
- Virtualized GPU Environments (NVIDIA vGPU, MIG, GPUDirect RDMA)
- Model Serving Frameworks (vLLM, TGI, Triton)
- Distributed Inference Topologies
- High Throughput, Low Latency Stateful Workloads on Kubernetes
- AI Serving & Distributed Compute (Ray, vLLM, SGLang, LeaderWorkerSets (LWS), vLLM-Orchestrator)
- Vector & NoSQL Databases (Milvus, Qdrant, Pinecone, Apache Cassandra, ScyllaDB)
- In Memory & Caching (Oracle Coherence, Hazelcast, Redis, Apache Ignite)
- Messaging & Streaming (Apache Kafka, RabbitMQ, Pulsar)
- Data & Analytics Engines (Apache Spark, Apache Flink, Dask, Trino/Presto)
- Go
- Python
- Java
- Systems Level Infrastructure Development
- Kubernetes Controllers
- Custom AI Wrappers
- Automation Frameworks
- Competing Enterprise Distributions (OpenShift, EKS, GKE, AKS, Rancher RKE/RKS)
- Networking (CSI/CNI, RDMA, InfiniBand, RoCEv2)
- Storage Abstractions
Experience Level
- Principal Level Engineering Stature
- 17+ 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 132,200.00 To USD 211,500.00
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.