Senior Forward Deployed Solution Engineer

Spectro Cloud

. Translate customer business or mission goals into deployable architectures .

Posted 10/30/2026full-timeUnited StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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

Demonstrates expertise in designing, deploying, and troubleshooting Kubernetes clusters and cloud-native applications, with strong proficiency in automation tools and programming languages such as Go or Python. Capable of effectively communicating technical concepts to diverse stakeholders and guiding deployments through all phases from initial capability to production readiness.

Highest-signal resume keywords

Kubernetes Cluster ManagementInfrastructure As CodeCloud-Native Application DeploymentCI/CD ImplementationLinux Fundamentals

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

KubernetesGoPythonTerraformAnsibleGitHub ActionsGitLab CINetworking FundamentalsAI Data LifecycleGPU-Enabled Infrastructure

Soft Skills

Effective CommunicationProblem-SolvingPrioritizationCollaborationAdaptability

Tools & Technologies

PaletteAICluster APIHelmKustomizeArgo CDFluxVMwareOpenStackProxmoxNVIDIA Software Ecosystem

Certifications & Qualifications

CKACKSCKAD

Industry Keywords

Cloud ComputingDevOpsSite Reliability EngineeringInfrastructure EngineeringAI NetworkingDistributed SystemsHigh-Performance NetworkingMulti-TenancySecurityRegulated Environments

Tech Stack

Tools & technologies

AnsibleCloudDNSFluxJenkinsKubernetesLinuxOpenStackPythonSwitchingTCP/IPTerraformVMwareGo

About the role

Key responsibilities & impact

Translate customer business or mission goals into deployable architectures
Build automation and integrations needed to implement those architectures
Guide deployments from first working capability through production readiness, scaled adoption, and operational handoff
Troubleshoot production Kubernetes clusters and cloud-native applications
Work across Palette / PaletteAI, Kubernetes, cloud, bare metal, edge, GPU systems, data pipelines, high-performance networking, and the NVIDIA software and hardware ecosystem
Collaborate with FDE practice resources and the aligned Deployment Strategist
Communicate with customer leaders, engineers, operators, executives, and nontechnical stakeholders
Conduct customer discovery, deployment, testing, and operational milestones onsite when required
Bring deployment learnings back to Product and Engineering to improve standardization, speed, and independent operability

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Computer Engineering, Information Systems, or a related technical field, or equivalent practical experience
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 ability to produce maintainable, tested, production-quality code
Hands-on experience with infrastructure as code and configuration automation using Terraform, Ansible, Cluster API, Helm, Kustomize, or equivalent tools
Experience implementing CI/CD or GitOps workflows using GitHub Actions, GitLab CI, Jenkins, Argo CD, Flux, or comparable systems
Working knowledge of Kubernetes security and multi-tenancy, including RBAC, namespaces, network policy, admission controls, quotas, secrets, pod security, and tenant isolation
Experience with at least one major public cloud 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
Working knowledge of the AI data and model lifecycle from data ingestion through model deployment, inference, monitoring, and feedback
Solid 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
Ability to communicate architecture, tradeoffs, status, and risk clearly to engineers, operators, executives, and nontechnical stakeholders
Demonstrated ability to work through ambiguous requirements, prioritize effectively, and deliver iterative value in a fast-moving environment
Ability to work onsite with Customer for discovery, deployment, testing, and operational milestones
Authorization to work in the stated location and ability to satisfy customer-specific access requirements
Preferred: 8+ years of relevant experience
Preferred: Current CKA, CKS, CKAD, relevant cloud, networking, security, or NVIDIA certification
Preferred: Experience with Spectro Cloud Palette / PaletteAI, Cluster API, Kubernetes fleet management, edge Kubernetes, or declarative full-stack lifecycle platforms
Preferred: Experience with NVIDIA GPU, networking, storage, AI platform, disconnected, regulated, or classified environments and related technologies

Benefits

Comp & perks

Opportunity to make a big impact every day
Opportunity to grow skills, push boundaries, and change the world
Diverse, collaborative work environment
Work with innovative technology and customer-focused teams
Fully remote work arrangement