Senior Forward Deployed Solution Engineer
New
J
JobgetherAI infrastructure
Fully remote position within the United States.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- At least 6 years of professional experience
- Required Skills
- PythonKubernetesGoCI/CDLinuxTerraformAnsible
Requirements
- 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.
- Bachelor’s degree in Computer Science, Computer Engineering, Information Systems, or a related technical discipline, or equivalent practical experience.
- Production experience designing, deploying, upgrading, and troubleshooting Kubernetes clusters and cloud-native applications.
- Strong Linux and distributed-systems fundamentals, including networking, storage, identity, certificates, resource management, and failure analysis.
- Proficiency in at least one modern programming language, preferably Go or Python, and ability to develop maintainable, tested, production-quality code.
- Hands-on experience with infrastructure as code and configuration automation, such as Terraform, Ansible, Cluster API, Helm, or 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, network policies, admission controls, quotas, secrets, and tenant isolation.
- Experience with at least one major public cloud provider and a datacenter deployment model such as bare metal, VMware, KubeVirt, Proxmox, or OpenStack.
- Working knowledge of GPU-enabled infrastructure and the AI data and model lifecycle.
- Strong networking fundamentals, including TCP/IP, routing, switching, DNS, BGP, load balancing, overlays, and packet-level troubleshooting; familiarity with high-performance networking concepts such as RDMA, RoCE, and InfiniBand.
- Willingness and ability to work onsite with customers for discovery, deployment, testing, and operational milestones.
Responsibilities
- Lead customer technical discovery and translate business, operational, and mission requirements into 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 for repeatable deployments and reliable operations.
- Guide customer implementations from initial working capabilities through production readiness, scaled adoption, and operational handoff.
- Design and support infrastructure for AI workloads, including GPU provisioning, Kubernetes scheduling, telemetry, and workload frameworks.
- Support AI data and model workflows from ingestion and preparation through deployment, inference, monitoring, and feedback.
- Troubleshoot distributed-systems and networking issues, including routing, switching, DNS, load balancing, latency, congestion, and packet-level problems.
- Apply infrastructure-as-code, configuration automation, CI/CD, and GitOps practices to improve deployment consistency.
- Collaborate with customer teams and internal Product and Engineering stakeholders, documenting architectures and sharing implementation feedback.
- Travel onsite to customer locations when needed for discovery, deployment, testing, and operational milestones.
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