Forward Deployed Engineer (Staff)

Broadcom•USA-TN-Remote Location, TN

4d•$110,800 - $177,300

About The Position

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. In this role, you will be embedded directly alongside enterprise client teams throughout the entire end to end lifecycle of an engagement. From initial architecture, bare metal/legacy re platforming, and AI cluster setup to live in the field troubleshooting and production rollout. Because you are part of the core Engineering organization, you won't just file bug reports; you will write production code in the field, build prototype integrations, and channel those contributions directly back to our engineering teams across the Infrastructure Software Division (ISG) to help shape, prioritize, and accelerate high impact platform capabilities. This role sits directly within the Engineering Business Unit (BU), working side by side with product and core software teams. Unlike traditional professional services or post sales support roles, our Forward Deployed Engineering team operates as an extension of core engineering in the field. Our Core Mission: Reduce customer adoption friction, dramatically shorten software iteration cycles, and establish a high bandwidth, direct feedback loop between real world enterprise deployments and product development.

Requirements

Engineering Mindset & Organizational Fit: Deep alignment with product engineering workflows; experience functioning within or closely alongside core software/R&D divisions rather than pure IT or professional services.
Full Lifecycle Engagement Experience: Proven track record of staying deeply engaged with customer technical leadership and developers across long term, complex engineering projects.
Product Minded Engineering: Demonstrated ability to translate raw technical customer requirements and field workarounds into clean product specifications and feature requests for core development teams.
AI & Accelerated Compute Infrastructure: Hands on experience operating GPU accelerated Kubernetes nodes, model serving runtimes (vLLM, TGI, Triton Inference Server), vector databases (Milvus, Qdrant, Pgvector), and distributed AI orchestration (Ray, KubeRay).
Distributed Systems & Stateful Workloads: Hands on experience containerizing and operating high throughput/low latency platforms:Distributed Caching & In Memory Grids: Redis, Oracle Coherence, Hazelcast
Compute & Analytics Engines: Risk calculation platforms, Hadoop (HDFS/YARN)
Messaging & Streaming: Apache Kafka, RabbitMQ, Pulsar
Cloud Native Data & AI Stack Depth: Deep technical knowledge of deploying, tuning, and scaling containerized data services, AI engines, and orchestrators:
Batch, Real Time & AI Processing: Ray, vLLM, Apache Spark, Apache Flink, Dask
Orchestration & Workflow: Apache Airflow, Prefect, Dagster, Argo Workflows
Query, NoSQL & Vector Engines: Trino/Presto, Apache Cassandra / ScyllaDB, Elasticsearch / OpenSearch, Milvus, Qdrant
Multi Kubernetes Proficiency: Technical depth across competing enterprise distributions (OpenShift, EKS, GKE, AKS, or Rancher RKE/RKS).
Infrastructure as Code & GitOps: Advanced skills in Terraform, Helm, Kubernetes Operators (KPO), Cluster API (CAPI), and GitOps (ArgoCD, Flux).
Bachelor's degree preferred. Relevant year's experience in lieu of a degree may be considered.
12+ years related experience required

Responsibilities

Direct Engineering to Field Collaboration: 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.
Rapid Iteration & Friction Reduction: Identify recurring migration blockers and platform usability gaps in real world customer environments, rapidly building and testing field fixes to shorten feature iteration cycles from months to days.
End to End Engagement Ownership: Stay actively embedded with customer technical teams from pre migration discovery through go live and operational stabilization, ensuring successful platform adoption and high customer trust.
Hands on Field Implementation & Troubleshooting: 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.
Engineering Feedback Loop & Feature Prioritization: Synthesize field tested code, architectural patterns, and customer pain points directly into core engineering requirements. Partner with product managers and core engineers to translate customer contributions into prioritized platform features.
VKS & AI Infrastructure Architecture: Architect, deploy, and maintain production grade vSphere Kubernetes Service (VKS) clusters across VMware Cloud Foundation (VCF) and hybrid cloud infrastructure, optimized for both general purpose compute and accelerated GPU/NPU workloads.
Bare Metal & Legacy Platform Migration: Lead technical migration strategies transitioning enterprise platforms, massive bare metal environments, and legacy data stacks over to VKS or cloud native Kubernetes targets.
Enterprise AI & Inferencing Modernization: Deploy, tune, and scale production AI inferencing workloads, RAG (Retrieval Augmented Generation) architectures, vector search, and model serving frameworks on Kubernetes using virtualized GPU resources (NVIDIA vGPU, MIG).
Data Services & Messaging Re Platforming: Containerize, refactor, and migrate heavy stateful engines, message brokers (Apache Kafka, RabbitMQ), distributed caches (Redis, Oracle Coherence, Hazelcast), and risk calculation/analytics platforms onto Kubernetes.
Modern Data & AI Stack Modernization: Architect operator driven, cloud native deployments for data processing, orchestration, and distributed AI frameworks (Ray, vLLM, Apache Spark, Apache Airflow, Trino/Presto, Flink, Dask, Cassandra/ScyllaDB, Milvus/Qdrant).
Multi Kubernetes Integration: Guide clients evaluating or operating VKS alongside competing distributions (Red Hat OpenShift, Amazon EKS, Google GKE, Azure AKS, Rancher RKE/RKS).