Senior Software Engineer

Who We Are
Spectro Cloud helps platform teams and cloud providers modernize and manage infrastructure for the AI era without adding more tools or operational complexity.

With PaletteAI, enterprises, public sector organizations, neoclouds and sovereign clouds can build, govern and operate full-stack environments across VMs, Kubernetes, edge, regulated and air-gapped locations, and AI infrastructure. PaletteAI Launchpads help teams start quickly with urgent outcomes such as VMware migration, token cost control or edge modernization, then scale into enterprise-wide lifecycle management, governance and fleet operations on the same platform. the Team

About the Team

Spectro Cloud is seeking a hands-on Senior Software Engineer to own the resolution of complex, customer-impacting issues in production. This role is the engineering backbone behind customer success, translating field escalations into durable code fixes, backports, and long-term product improvements across commercial and federal deployments.

As a Senior Software Engineer, you will operate at the intersection of Support, Product Engineering, and Customer Reliability. You will debug deep into Kubernetes internals, Go microservices, and distributed systems; deliver patches and hotfixes on supported release branches; and drive systemic fixes that prevent recurrence. You will be a technical authority for high-severity escalations and a trusted partner to customers, TAMs, and field engineers.

About the Role
Your core mandate is to keep customer environments stable, supported, and current — while continuously improving the maintainability of the shipping product.
• Root-cause analysis and code-level fixes for customer-reported defects across the Palette platform, Kubernetes control plane and VM deployments.
• Go-based microservice debugging, patching, and backporting across multiple supported release branches.
• Kubernetes-native troubleshooting spanning CAPI, controllers, operators, CNI, CSI, and workload runtime behavior.
• Reproduction environments and test harnesses that convert customer scenarios into repeatable engineering artifacts.
• Hotfix delivery, patch releases, and CVE remediation on supported versions with strict quality and regression discipline.
• Feedback loops into Product and Engineering that convert recurring escalations into permanent product improvements.

Success in this role requires a builder-and-debugger mindset. You should be equally comfortable reading a stack trace, reading Go source, and engaging with a customer. You will use AI responsibly to accelerate triage, log analysis, and reproduction — while maintaining strong engineering accountability for every fix that ships.

Responsibilities
1. Escalation Ownership & Root Cause Analysis
• Own high-severity customer escalations end to end — from initial reproduction through code-level root cause, fix, verification, and customer confirmation.
• Debug complex Kubernetes and distributed-systems issues across control plane, cluster lifecycle, networking, storage, and workload runtime.
• Produce clear, technically rigorous RCA documents that hold up to customer, field, and executive scrutiny.
• Partner with Support, TAMs, and Customer Success to keep customers informed and unblock production impact quickly.

2. Code Fixes, Backports & Patch Delivery
• Deliver production-quality fixes in Go across the Palette codebase and Kubernetes-native components.
• Backport fixes cleanly across multiple supported release branches with strong regression discipline.
• Drive hotfix, patch, and CVE releases through the Software release train, coordinating with QA, Release Engineering, and Product.
• Maintain high code-review standards on code changes — small, safe, well-tested, and well-documented.

3. Reproduction, Test Coverage & Regression Prevention
• Build reproduction environments and minimal test cases that convert one-off customer scenarios into permanent engineering assets.
• Expand unit, integration, and end-to-end test coverage to prevent regression of every fix that ships.
• Partner with QA to harden test suites against the failure modes seen in the field.
• Identify systemic gaps in observability, error handling, or upgrade paths and drive them to closure.

4. Product Feedback & Long-Term Hardening
• Identify recurring escalation patterns and drive engineering changes that eliminate their root causes. • Partner with Product Management and feature teams to feed development insights into roadmap and design reviews.
|• Improve upgrade, rollback, and day-2 operations based on real-world customer signals.
• Contribute to supportability improvements — logs, diagnostics, must-gather tooling, and self-service remediation.

5. AI-Accelerated Software Development (Responsible Innovation)
• Apply generative AI tools responsibly to accelerate log triage, stack trace analysis, reproduction scaffolding, and RCA drafting.
• Use effective prompt-engineering practices to improve the consistency and quality of AI-assisted debugging workflows.
• Validate all AI-generated artifacts before use. Increased velocity must never compromise engineering correctness, security, or compliance.

6. Clarity, Precision, Documentation-Driven Engineering (DDE)