Technical Lead, QA

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 Release Engineering QA team at Spectro Cloud owns the release quality of our core platforms. We are the final gate before every GA, patch, and hotfix reaches enterprise customers.

We operate at the intersection of modern cloud-native technologies and release engineering discipline.

The team designs and maintains automated release pipelines and regression, upgrade, and compatibility test suites across APIs, UIs, and backend services, while pioneering AI-assisted workflows to accelerate test design, execution, and coverage. Given the breadth of our enterprise offerings, we certify complex release matrices across diverse Kubernetes distributions (such as K8s, K3s, and RKE2), operating systems, cloud providers, edge topologies, and air-gapped environments.

As release quality advocates, we collaborate closely with Software Engineering, Dev Ops, and Product teams throughout the release lifecycle. From defining release test strategies in Test Rail and establishing automated CI/CD release gates to validating upgrades and verifying bug fixes, our mission is to deliver dependable, enterprise-grade releases to our global customers on a predictable cadence.

• 12+ years of IT experience with a strong track record in software quality engineering and test automation for complex, distributed systems.
• Bachelor's or Master's degree in Computer Science, Information Technology, or a related field, or equivalent practical experience.
• Deep, hands-on experience designing and building test automation frameworks from the ground up for both API and UI (must-have).
• Subject-matter expertise in Kubernetes: architecture, operators, CRDs, scheduling, storage, and multi-cluster topologies (must-have).
• Subject-matter expertise in networking: TCP/IP, DNS, load balancing, CNI plugins, service mesh, ingress/egress, and network policies (must-have).
• Hands-on experience testing AI inference workloads: GPU-based serving, model deployment frameworks (e.g., Triton, vLLM, KServe), throughput/latency benchmarking, and inference accuracy validation (must-have).
• Expert-level proficiency in Go, with production-quality code; comfortable reading and contributing to product code, not only test code.
• Extensive hands-on experience with containers and cloud platforms (AWS, Azure, GCP, or VMware/bare-metal).
• Strong proficiency in Linux-based operating systems and low-level troubleshooting/debugging (must-have).
• Demonstrated experience leading test architecture and technical direction across multiple teams as a Staff or Principal-level individual contributor.
• Proven ability to influence engineering peers and leadership without direct authority.
• Strong understanding of modern testing methodologies: contract testing, chaos/resilience testing, performance and scale testing, and CI/CD-driven quality gates.

• Deep experience leading development of automation using Go-based test frameworks like Godog at scale.
• Experience testing Kubernetes lifecycle management, cluster APIs (CAPI), or edge/IoT Kubernetes deployments.
• Experience with LLM/GenAI inference stacks, distributed inference, and GPU cluster operations.
• CKA, CKAD, or CKS certification.
• Experience with observability stacks (Prometheus, Grafana, Open Telemetry) applied to test infrastructure.
• Prior contributions to open-source Kubernetes or cloud-native testing projects.
• Track record of setting and delivering on multi-quarter technical strategy for quality/automation organizations.
• Experience partnering with customer-facing teams on escalations and driving preventative quality improvements from field learnings.

• The On-Target Earnings for this role is $280,000-$304,0000 + Equity.
• Comprehensive Benefits:
  Medical, dental, and vision insurance, including 100% employer-paid base-level coverage for employees.
• Retirement Savings:
  Access to a…