Forward Deployed Engineer – Staff

Broadcom

Staff Forward Deployed Engineer modernizing Kubernetes, stateful platforms, and AI infrastructure for Broadcom enterprise clients. Embedding with customers to deploy, troubleshoot, and feed production solutions into core engineering.

Posted 9/9/2026full-timeRemote • Colorado, New York, North Carolina, Tennessee, Texas • 🇺🇸 United StatesLead💰 $110,800 - $177,300 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in architecting, deploying, and maintaining production-grade VKS clusters and Kubernetes environments, with a strong focus on optimizing AI inferencing workloads and containerized data services. Proven ability to translate technical requirements into actionable product specifications while leading complex engineering projects in collaboration with cross-functional teams.

Highest-signal resume keywords

Kubernetes ExpertiseAI Inferencing WorkloadsContainerization and MigrationDistributed AI OrchestrationTechnical Leadership Engagement

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

KubernetesGPU Accelerated NodesModel Serving RuntimesVector DatabasesDistributed AI OrchestrationContainerizationHigh Throughput PlatformsTerraformHelmApache Kafka

Soft Skills

Technical CommunicationCustomer EngagementProblem Solving

Tools & Technologies

VMware Cloud FoundationOpenShiftEKSGKEAKSRedisApache SparkApache FlinkElasticsearchArgo Workflows

Industry Keywords

Infrastructure SoftwareMigration StrategiesCloud-Native DeploymentsData ProcessingOrchestration

Tech Stack

Tools & technologies

AirflowApacheCassandraCloudElasticSearchFluxHadoopHDFSKafkaKubernetesOpenShiftOraclePulsarRabbitMQRayRedisSparkTerraformVMwareYarn

About the role

Key responsibilities & impact

Act as an embedded engineering liaison across the Infrastructure Software Division
Work directly with core software architects, product managers, and enterprise client developers to eliminate deployment friction
Identify recurring migration blockers and platform usability gaps in customer environments
Build and test field fixes to shorten feature iteration cycles
Own engagements from pre-migration discovery through go-live and operational stabilization
Work alongside client engineers in production environments
Write code, build manifests, debug live networking, storage, and GPU failures, and optimize VKS performance
Translate field-tested code, architectural patterns, and customer pain points into prioritized platform features
Architect, deploy, and maintain production-grade VKS clusters across VMware Cloud Foundation and hybrid cloud infrastructure
Lead migration strategies for enterprise platforms, massive bare-metal environments, and legacy data stacks to VKS or cloud-native Kubernetes
Deploy, tune, and scale production AI inferencing workloads, RAG architectures, vector search, and model-serving frameworks on Kubernetes
Containerize, refactor, and migrate 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 OpenShift, EKS, GKE, AKS, or Rancher distributions

Requirements

What you’ll need

12+ years related experience required
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
Ability to translate raw technical customer requirements and field workarounds into clean product specifications and feature requests
Hands-on experience operating GPU accelerated Kubernetes nodes
Experience with model serving runtimes including vLLM, TGI, and Triton Inference Server
Experience with vector databases including Milvus, Qdrant, and Pgvector
Experience with distributed AI orchestration including Ray and KubeRay
Hands-on experience containerizing and operating high throughput/low latency platforms
Experience with Redis, Oracle Coherence, Hazelcast, Hadoop (HDFS/YARN), Apache Kafka, RabbitMQ, and Pulsar
Deep technical knowledge of containerized data services, AI engines, and orchestrators
Experience with Ray, vLLM, Apache Spark, Apache Flink, Dask, Apache Airflow, Prefect, Dagster, Argo Workflows, Trino/Presto, Apache Cassandra/ScyllaDB, Elasticsearch/OpenSearch, Milvus, and Qdrant
Technical depth across OpenShift, EKS, GKE, AKS, or Rancher RKE/RKS
Advanced skills in Terraform, Helm, Kubernetes Operators (KPO), Cluster API (CAPI), and GitOps tools including ArgoCD and Flux
Bachelor's degree preferred; relevant years of experience in lieu of a degree may be considered

Benefits

Comp & perks

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
Paid Family Leave and other leaves of absence in accordance with applicable laws