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NEXTMOVEFDE careers · United States

Forward Deployed Engineer (Staff)

AI summary of the role

Staff-level Forward Deployed Engineer embedded with enterprise clients to drive Kubernetes platform modernization, migration of stateful workloads, and AI infrastructure adoption.

What you’ll do

  • Embed with enterprise clients across the full lifecycle from architecture to production rollout, writing production code and troubleshooting live issues.
  • Architect and maintain production-grade VKS clusters on VMware Cloud Foundation and hybrid cloud, optimized for GPU/NPU workloads.
  • Lead migration of bare-metal and legacy data stacks to Kubernetes, including stateful engines, messaging, and caches.
  • Deploy and scale AI inferencing workloads, RAG architectures, and model serving on Kubernetes with virtualized GPUs.

What you’ll bring

  • 12+ years related experience required.
  • Deep hands-on experience with Kubernetes and containerized stateful workloads (Kafka, Redis, Cassandra, etc.).
  • Experience with GPU-accelerated Kubernetes, model serving (vLLM, TGI, Triton), and distributed AI (Ray).
  • Proficiency with competing Kubernetes distributions (OpenShift, EKS, GKE, AKS, RKE).

Technologies

Kubernetes · VKS · VMware Cloud Foundation · vGPU · MIG · NVIDIA · Kafka · RabbitMQ · Redis · Oracle Coherence · Hazelcast · Ray

Source and classification

Implementation & delivery · Evidence for this classification:

Please Note: 1. If you are a first time user, please create your candidate login account before you apply for a job. (Click Sign In > Create Account) 2. If you already have a Candidate Account, please Sign-In before you apply. Job Description: 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
More from the job description

Please Note: 1. If you are a first time user, please create your candidate login account before you apply for a job. (Click Sign In > Create Account) 2. If you already have a Candidate Account, please Sign-In before you apply. Job Description: 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 Cor [... source excerpt omitted ...] ction, dramatically shorten software iteration cycles, and establish a high bandwidth, direct feedback loop between real world enterprise deployments and product development. Key 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 d [... source excerpt omitted ...] ip: 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

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