Senior Software Engineer

About the Team

Spectro Cloud, a recognized leader in enterprise Kubernetes management with its flagship Palette platform, 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.

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