Senior Forward Deployed Solution Engineer

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.

• 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…