Senior customer-facing engineer leading complex US customer deployments spanning Kubernetes, cloud/bare-metal infrastructure, GPU/AI platforms, and high-performance networking — designing architectures, building automation, and guiding deployments through production handoff. Requires 6+ years in software/platform/DevOps/SRE roles, deep Kubernetes experience, and Go or Python.
Compensation
Not disclosed
Location
US
Work setup
Remote
Seniority
Senior
Employment
Full time
Experience
6+ years
Category
Solutions engineering
Posted
Skills
ai
ansible
api
argocd
automation
bgp
ci-cd
cloud
cloud-native
cuda
data-ingestion
data-lake
data-pipelines
devops
distributed-systems
distributed-training
dns
ethernet
evpn
firmware
flux
gdpr
github
github-actions
gitlab
gitops
gpu
helm
infiniband
infrastructure-as-code
jenkins
kubeflow
kubernetes
kustomize
linux
llm
model-deployment
networking
nim
openstack
proxmox
python
rbac
rdma
secrets-management
slurm
tcp-ip
technical-discovery
tensorrt
terraform
triton
vector-databases
vllm
vmware
vxlan
Must have
Bachelor's degree in CS, Computer Engineering, Information Systems, or related, or equivalent practical experience
6+ years in software, platform, SRE, DevOps, infrastructure, or solutions engineering or comparable customer-facing technical role
Production experience designing, deploying, upgrading, and troubleshooting Kubernetes clusters and cloud-native applications
Strong Linux and distributed-systems fundamentals
Proficiency in at least one modern programming language, preferably Go or Python
Hands-on experience with IaC and config automation tools such as Terraform, Ansible, Cluster API, Helm, Kustomize
Experience implementing CI/CD or GitOps workflows using GitHub Actions, GitLab CI, Jenkins, Argo CD, or Flux
Working knowledge of Kubernetes security and multi-tenancy (RBAC, network policies, admission controls, quotas, secrets, tenant isolation)
Experience with at least one major public cloud provider and one datacenter deployment model (bare metal, VMware, KubeVirt, Proxmox, OpenStack)
Working knowledge of GPU-enabled infrastructure: firmware, drivers, CUDA, container runtimes, Kubernetes scheduling, device plugins, telemetry
Understanding of the AI data and model lifecycle from ingestion through deployment, inference, monitoring, and feedback
Familiarity with high-performance AI networking: RDMA, RoCE, InfiniBand, SR-IOV, GPUDirect, collective communication
Nice to have
8+ years of relevant experience including ownership of complex production deployments or strategic customer programs
Direct experience with Palette, PaletteAI, Cluster API, Kubernetes fleet management, edge Kubernetes, or declarative lifecycle platforms
Current CKA, CKS, CKAD, or relevant cloud, networking, security, or NVIDIA certifications
Production experience with NVIDIA Cloud Partner (NCP) or comparable multi-tenant GPU cloud environments
Hands-on experience with NVIDIA GPU Operator, Network Operator, DCGM, MIG, vGPU, NIM, Triton, TensorRT-LLM, vLLM, NCCL, or NVIDIA AI Enterprise
Experience with GPU schedulers and AI platforms such as Slurm, Volcano, KAI, Ray, Kubeflow, or ClearML
Distributed training or high-throughput inference experience on H100/H200/B200/GB200/GB300 or similar accelerators
Hands-on experience with NVIDIA Spectrum-X, Quantum InfiniBand, ConnectX, BlueField DPUs/SuperNICs, or DOCA
Experience building or operating SONiC or Cumulus Linux leaf-spine fabrics, including FRR, BGP, EVPN/VXLAN
Deep expertise in tenant isolation across bare metal, VMs, Kubernetes control planes, GPU resources, and high-performance networks
Experience with high-performance storage and AI data platforms: object storage, NVMe-oF, parallel file systems, vector databases, model registries
Experience working in disconnected, air-gapped, sovereign, regulated, safety-sensitive, or classified environments
Record of contributing reusable software, open-source projects, reference architectures, or technical publications
Full description
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Forward Deployed Solution Engineer based in the United States.
The Senior Forward Deployed Solution Engineer will lead complex customer deployments, translating strategic business and mission objectives into reliable, production-ready technical solutions. This hands-on engineering role combines software development, Kubernetes infrastructure, cloud and bare-metal systems, networking, and AI infrastructure. You will work directly with customer engineering teams, operators, and executives to design architectures, build automation, troubleshoot systems, and guide deployments from initial discovery through operational handoff. The scope spans cloud-native platforms, edge environments, GPU infrastructure, high-performance networking, and AI data and model workflows. You will collaborate with other forward-deployed engineering resources and internal Product and Engineering teams to turn deployment experience into reusable capabilities and standardized practices. This is an opportunity for an experienced technical leader who enjoys solving ambiguous problems and delivering solutions that customers can operate and scale independently.
Accountabilities
You will serve as a senior technical lead throughout complex customer engagements, taking responsibility for translating requirements into deployable architectures and ensuring that solutions deliver lasting operational and business value.
- Lead technical discovery with customers, translating business objectives, operational constraints, and mission requirements into practical technical architectures and deployment plans.
- Design, deploy, upgrade, and troubleshoot production Kubernetes clusters and cloud-native applications across public cloud, bare-metal, datacenter, and edge environments.
- Build automation, integrations, infrastructure tooling, and maintainable software to support repeatable deployments and reliable operations.
- Guide customer implementations from initial working capabilities through production readiness, scaled adoption, and operational handoff.
- Work across Kubernetes platforms, cloud infrastructure, Linux systems, GPU-enabled environments, data pipelines, and high-performance networking technologies.
- Design and support infrastructure for AI workloads, including GPU provisioning, drivers, container runtimes, Kubernetes scheduling, telemetry, and workload frameworks.
- Support the implementation and operation of AI data and model workflows, from data ingestion and preparation through deployment, inference, monitoring, and feedback.
Senior Forward Deployed Solution Engineer at Jobgether (Remote) — aijobsearch
Excellent communication with engineers, operators, executives, and nontechnical stakeholders
Ability to navigate ambiguous requirements, prioritize effectively, and deliver incrementally
Willingness and ability to travel onsite to customer locations for discovery, deployment, testing, and operational milestones
Authorization to work in the United States and ability to meet customer-specific access requirements
- Troubleshoot complex distributed systems and networking issues, including routing, switching, DNS, load balancing, overlays, latency, bandwidth, congestion, and packet-level problems.
- Apply infrastructure-as-code, configuration automation, CI/CD, and GitOps practices to improve deployment consistency and operational efficiency.
- Address Kubernetes security and multi-tenancy requirements, including access controls, network policies, admission controls, resource quotas, secrets management, and tenant isolation.
- Collaborate with customer engineers, operators, technical leaders, and executives, clearly communicating architectural decisions, tradeoffs, delivery status, and emerging risks.
- Work closely with other forward-deployed engineering resources and deployment strategy stakeholders to coordinate technical execution and customer outcomes.
- Identify delivery risks early, communicate changes directly, and prioritize work to maintain progress in complex or evolving environments.
- Document architectures, configurations, operational procedures, and lessons learned to support customer independence and future deployments.
- Share implementation feedback with Product and Engineering teams, helping improve platform capabilities, automation, reference architectures, and deployment practices.
- Travel onsite to customer locations when needed for discovery, deployment, testing, and key operational milestones.
Requirements
The ideal candidate combines strong Kubernetes and infrastructure engineering expertise with software development skills and sound customer-facing judgment. You should be comfortable moving between code, architecture, infrastructure, networking, and stakeholder discussions while maintaining a clear focus on customer outcomes.
- Bachelor’s degree in Computer Science, Computer Engineering, Information Systems, or a related technical discipline, or equivalent practical experience.
- At least 6 years of professional experience in software engineering, platform engineering, site reliability engineering, DevOps, infrastructure engineering, solutions engineering, or a comparable customer-facing technical role.
- Production experience designing, deploying, upgrading, and troubleshooting Kubernetes clusters and cloud-native applications.
- Strong Linux and distributed-systems fundamentals, including processes, networking, storage, identity, certificates, resource management, and failure analysis.
- Proficiency in at least one modern programming language, preferably Go or Python, with the ability to develop maintainable, tested, production-quality code.
- Hands-on experience with infrastructure as code and configuration automation tools such as Terraform, Ansible, Cluster API, Helm, and Kustomize, or equivalent technologies.
- Experience implementing CI/CD or GitOps workflows using tools such as GitHub Actions, GitLab CI, Jenkins, Argo CD, or Flux.
- Working knowledge of Kubernetes security and multi-tenancy, including RBAC, namespaces, network policies, admission controls, quotas, secrets, pod security, and tenant isolation.
- Experience with at least one major public cloud provider and at least one datacenter deployment model, such as bare metal, VMware, KubeVirt, Proxmox, or OpenStack.
- Working knowledge of GPU-enabled infrastructure, including firmware, drivers, CUDA, container runtimes, Kubernetes scheduling, device plugins, telemetry, and workload frameworks.
- Understanding of the AI data and model lifecycle, from data ingestion through model deployment, inference, monitoring, and feedback.
- Strong networking fundamentals, including TCP/IP, routing, switching, DNS, BGP, load balancing, overlays, MTU, latency, bandwidth, congestion, and packet-level troubleshooting.
- Familiarity with high-performance AI networking concepts such as RDMA, RoCE, InfiniBand, SR-IOV, GPUDirect, and collective communication.
- Excellent communication skills, with the ability to explain architecture, technical tradeoffs, project status, and risks to engineers, operators, executives, and nontechnical stakeholders.
- Demonstrated ability to navigate ambiguous requirements, prioritize effectively, and deliver incremental value in a fast-moving environment.
- Willingness and ability to work onsite with customers for discovery, deployment, testing, and operational milestones.
- Authorization to work in the United States and ability to meet customer-specific access requirements.
The following qualifications are considered strong advantages:
- 8 or more years of relevant experience, including ownership of complex production deployments or strategic customer programs.
- Direct experience with Palette, PaletteAI, Cluster API, Kubernetes fleet management, edge Kubernetes, or declarative full-stack lifecycle platforms.
- Current CKA, CKS, CKAD, or relevant cloud, networking, security, or NVIDIA certifications.
- Production experience designing or operating NVIDIA Cloud Partner (NCP) or comparable multi-tenant GPU cloud environments.
- Deep expertise in tenant isolation across bare-metal infrastructure, virtual machines, Kubernetes control planes, GPU resources, storage, and high-performance networks.
- Hands-on experience with NVIDIA GPU Operator, Network Operator, DCGM, MIG, vGPU, NIM, Triton, TensorRT-LLM, vLLM, NCCL, or NVIDIA AI Enterprise.
- Experience with GPU schedulers and AI platform technologies such as Run, Slurm, Volcano, KAI, Ray, Kubeflow, or ClearML.
- Experience with distributed training or high-throughput inference using H100, H200, B200, GB200, GB300, or other modern accelerator platforms.
- Hands-on experience designing or operating NVIDIA Spectrum-X Ethernet fabrics, Quantum InfiniBand, ConnectX adapters, BlueField DPUs or SuperNICs, DOCA, or comparable high-speed, low-latency networking technologies.
- Experience building or operating SONiC or Cumulus Linux leaf-spine fabrics, including FRR, BGP, EVPN/VXLAN, VRF, zero-touch provisioning, automated validation, upgrades, and telemetry.
- Experience integrating BlueField in DPU or restricted modes for network, security, storage, isolation, or telemetry offload, as well as familiarity with DPU provisioning, BMC, Redfish, attestation, and lifecycle management.
- Experience with high-performance storage and AI data platforms, including object storage, NVMe-oF, parallel file systems, data lakes, streaming, feature stores, vector databases, model registries, and metadata or lineage systems.
- Experience working in disconnected, air-gapped, sovereign, regulated, safety-sensitive, or classified environments.
- A record of contributing reusable software, open-source projects, reference architectures, technical publications, or customer enablement materials.
Benefits
- Fully remote position within the United States.
- Opportunity to work on advanced technologies spanning Kubernetes, cloud infrastructure, GPU systems, AI platforms, and high-performance networking.
- Direct involvement in complex, high-impact customer deployments and strategic technical initiatives.
- Broad technical ownership and the opportunity to influence customer architectures and operational outcomes.
- Close collaboration with experienced Field Engineering, Product, and Engineering teams.
- Opportunity to contribute to reusable software, automation, reference architectures, and improvements to future customer deployments.
- A collaborative, diverse, and fast-paced work environment that encourages innovation, professional growth, and meaningful technical contributions.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether?
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.