Forward Deployed Engineer - Kubernetes

Overview

Embedded Forward Deployed Engineer working alongside enterprise client teams across the full engagement lifecycle, focused on Kubernetes platform modernization, large-scale stateful workload migration and enterprise AI infrastructure, while feeding field findings back to core engineering in the Infrastructure Software Division.

What you'll do

• 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 in real-world customer environments, and rapidly build and test field fixes to shorten feature iteration cycles.
• Stay actively embedded with customer technical teams from pre-migration discovery through go-live and operational stabilization.
• Work shoulder to shoulder with client engineers in production environments to write code, build manifests, debug live networking, storage and GPU failures, and optimize VKS performance.
• Synthesize field-tested code, architectural patterns and customer pain points into core engineering requirements, and partner with product managers and core engineers to prioritize 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, bare metal environments and legacy data stacks 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 (NVIDIA vGPU, MIG).
• Containerize, refactor and migrate stateful engines, message brokers (Apache Kafka, RabbitMQ), distributed caches (Redis, Oracle Coherence, Hazelcast) and risk calculation/analytics platforms onto Kubernetes.
• 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).
• Guide clients evaluating or operating VKS alongside competing distributions (Red Hat OpenShift, Amazon EKS, Google GKE, Azure AKS, Rancher RKE/RKS).

What you'll need

• Engineering mindset and organizational fit: deep alignment with product engineering workflows, with 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: distributed caching and in-memory grids (Redis, Oracle Coherence, Hazelcast), compute and analytics engines (risk calculation platforms, Hadoop HDFS/YARN), and messaging and streaming (Apache Kafka, RabbitMQ, Pulsar).
• Deep technical knowledge of deploying, tuning and scaling containerized data services, AI engines and orchestrators, including batch, real-time and AI processing (Ray, vLLM, Apache Spark, Apache Flink, Dask), orchestration and workflow (Apache Airflow, Prefect, Dagster, Argo Workflows), and query, NoSQL and vector engines (Trino/Presto, Apache Cassandra / ScyllaDB, Elasticsearch / OpenSearch, Milvus, Qdrant).
• Technical depth across competing enterprise Kubernetes distributions (OpenShift, EKS, GKE, AKS, or Rancher RKE/RKS).
• Advanced skills in Infrastructure as Code and GitOps: Terraform, Helm, Kubernetes Operators (KPO), Cluster API (CAPI), and GitOps (ArgoCD, Flux).
• 12+ years of related experience required.

Nice to have

• Bachelor's degree preferred; relevant years of experience in lieu of a degree may be considered.

Details

• Location: USA, Tennessee, Remote.
• Annual base salary range: USD 110,800.00 to USD 177,300.00, plus eligibility for a discretionary annual bonus and equity awards.