Forward Deployed Engineer – Staff

Broadcom

Staff Forward Deployed Engineer modernizing Kubernetes platforms, stateful workloads, and AI infrastructure for Broadcom enterprise clients. Embedding with engineering teams to deliver migrations, production code, and field fixes.

Posted 9/4/2026full-timeRemote • California • 🇺🇸 United StatesLead💰 $110,800 - $177,300 per yearWebsite

Core Competencies

Role fit

Core Competencies

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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 engage with technical leadership and translate complex requirements into actionable product specifications.

Highest-signal resume keywords

Kubernetes DeploymentGPU Accelerated NodesAI Inference WorkloadsContainerization of Stateful EnginesTerraform and Helm Proficiency

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

KubernetesGPU AccelerationAI Model ServingContainerizationData OrchestrationApache KafkaRedisHadoopTerraformHelm

Soft Skills

Technical LeadershipCustomer EngagementProblem SolvingCollaboration

Tools & Technologies

OpenShiftEKSGKEAKSRancherRayVLLMTriton Inference ServerMilvusQdrant

Industry Keywords

Infrastructure SoftwareCloud-NativeData ProcessingDistributed AIMigration Strategies

Tech Stack

Tools & technologies

AirflowApacheCassandraCloudElasticSearchFluxHadoopHDFSKafkaKubernetesOpenShiftOraclePulsarRabbitMQRayRedisSparkTerraformVMwareYarn

About the role

Key responsibilities & impact

Act as an embedded engineering liaison across the Infrastructure Software Division, working 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, rapidly building and testing field fixes
Own the engagement lifecycle from pre-migration discovery through go-live and operational stabilization
Work with client engineers in production environments to write code, build manifests, debug networking, storage, and GPU failures, and optimize VKS performance
Synthesize field-tested code, architectural patterns, and customer pain points into core engineering requirements and 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, large 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 using virtualized GPU resources
Containerize, refactor, and migrate stateful engines, message brokers, distributed caches, and risk calculation/analytics platforms onto Kubernetes
Architect operator-driven, cloud-native deployments for modern 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

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 (vLLM, TGI, Triton Inference Server), vector databases (Milvus, Qdrant, Pgvector), and distributed AI orchestration (Ray, KubeRay)
Hands on experience containerizing and operating high throughput/low latency platforms, including Redis, Oracle Coherence, Hazelcast, risk calculation platforms, Hadoop (HDFS/YARN), Apache Kafka, RabbitMQ, and Pulsar
Deep technical knowledge of deploying, tuning, and scaling containerized data services, AI engines, and orchestrators including 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 (ArgoCD, Flux)
Bachelor's degree preferred; relevant years' experience in lieu of a degree may be considered
12+ years related experience required

Benefits

Comp & perks

Discretionary annual bonus
Competitive new hire equity grant
Annual equity awards
Medical, dental and vision plans
401(K) participation including 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