Senior Solutions Engineer

About TensorWave Our mission is simple: deliver seamless, secure, reliable, and resilient AI compute at scale. We've built a versatile cloud platform that eliminates infrastructure barriers, empowering builders to focus on innovation instead of fighting their stack. Because breakthrough AI should move at the speed of ideas, not infrastructure. About the Role We're looking for a Senior Solutions Engineer to serve as the elite escalation point between our Global Operations Center (GOC) and our Core Engineering teams. You are the technical backstop for our most sophisticated customers teams training large models who cannot afford a single hour of downtime. You'll own the problems that go beyond runbooks, sitting at the intersection of customer success and engineering: resolving the hardest technical blockers and translating those findings into a more resilient product. If you're an engineer who loves the detective work of kernel-level debugging and high-performance networking, and who also thrives in the high stakes environment of customer-facing resolution, this is your role. What You’ll Do Resolve Complex Escalations: Act as the final authority on issues exceeding GOC scope, utilizing code-level debugging and architectural investigation. Direct Customer Engagement: Partner with customer technical leads to diagnose production issues, ensuring transparency and rapid resolution through active collaboration. Iterative Problem Solving: Develop diagnostic scripts and workarounds to maintain customer operations while long-term patches are in development. Drive Root Cause Analysis: Own end-to-end P1 resolution, partnering with TAMs to deliver clear, actionable post-incident analysis. Bridge to Engineering: Convert recurring customer pain points into evidence-based feature requests, influencing product roadmap to resolve systemic failures. Build Scalable Knowledge: Document non-obvious platform behaviors and refine GOC runbooks, ensuring institutional knowledge grows with every incident. Who You Are Required Qualifications 5–9 years in Infrastructure Engineering, Platform Engineering, or SRE, with a specific focus on high-performance computing or large-scale AI stacks. Pr oven track record of managing complex production environments where system reliability is mission-critical. Kubernetes Expert: Deep experience in cluster administration and scheduler internals; comfortable reading/modifying controller code. AI/GPU Infrastructure Specialist: Proficient in orchestrating GPU workloads and diagnosing training job failures using ROCm or CUDA. Network Pathologist: Skilled in RDMA/RoCEv2, SRIOV, and BGP; capable of interpreting switch telemetry to identify silent packet drops. Linux Power User: Expert in kernel networking, hugepages, and cgroups; able to debug at the OS layer when applications are silent. Builder Mindset: Proficient in Python and Ansible; capable of writing custom diagnostic tools to automate remediation.