Spectro Cloud

Own strategy and development for Kubernetes cluster lifecycle products across cloud and bare-metal environments, including AI workloads and migration of existing clusters. | Requires at least five years of infrastructure product-management experience, hands-on cloud and on-premise exposure, and deep Kubernetes and cluster-lifecycle expertise. | Who we are Spectro Cloud is the AI and Kubernetes infrastructure platform behind some of the most demanding environments in the world. Our platform lets enterprises manage the full infrastructure stack, from bare metal to production AI, across data centers, clouds, edge, and air-gapped environments, at fleet scale: some of our customers run thousands of clusters with a handful of engineers. About the role Kubernetes is becoming the common infrastructure abstraction under three classes of workloads: VMs, containers, and AI. Multi-cluster management is where that abstraction becomes real, and it's the foundation the rest of our platform stands on. Enterprises come to us with heterogeneous estates: public cloud and managed Kubernetes (AWS, Azure, Google Cloud, EKS, AKS, GKE, etc.), on-prem (vSphere, Nutanix, bare metal), all built on Cluster API, some created with our platform, others built outside it. Your job is to make every one of them a first-class citizen: declared, reconciled, and operated with the same rigor regardless of substrate, with no snowflakes. You will own one of the most established product areas in the company, with a large enterprise and public sector customer base, and drive its next chapter: making multi-cluster infrastructure the substrate that AI workloads run on, adopting clusters built outside our platform as first-class citizens, keeping pace with upstream Kubernetes and Cluster API, and turning long-term support into a product enterprises can buy. This is a deep domain role. You'll work daily with senior engineering leads and large enterprise customers. It rewards technical fluency and judgment about what not to build. What you will own Infrastructure for AI workloads: bare metal and private cloud as the high-performance substrate for GPU clusters, in partnership with our AI product area. Multi-cluster is where AI factories actually run. Cluster lifecycle across every substrate: provisioning, upgrades, scaling, and Day-2 operations for clusters on public clouds, private clouds, and bare metal, including the customer-facing side of platform transitions: when the ecosystem moves and clusters must follow, you own the migration experience and its communication. You own the "why and what"; engineering owns the how. Brownfield adoption and federation: the strategic direction of the area. Take clusters built outside our platform from read-only visibility to fully managed, without disruption. This is what wins large enterprise estates. Long-term support as a product: LTS for Kubernetes versions and workloads, vendor relationships that back it, and the version support policy customers plan against. This area has direct revenue attached. Estate organization and multi-tenancy: how enterprises carve up large cluster estates with tenant and team boundaries, shared versus dedicated clusters, bringing existing clusters under management, and the access and isolation model that makes one platform safe for many teams. This map will evolve. The portfolio is growing faster than the org chart, and PMs here regularly take ownership of new product areas as they emerge, including products not yet announced. Expect your scope to expand as the platform does, deliberately and in dialogue with you. How you will work Evidence over opinion: usage telemetry and customer signals over the loudest voice, including your own. Commitments are commitments: this area ships on release trains with enterprise customers planning around dates. Refined epics, explicit scope, honest slip communication. One org: PM, UX, and Docs operate as a single group; you bring design and documentation in at the definition stage. AI-native by default: we expect every PM to multiply their output with AI tooling, daily. Remote-first across US, EU, and India time zones. Clear async writing is how decisions happen here. Minimum Qualifications We don't expect the ideal candidate to check every box. The items below are what we look for; don't let a gap discourage you from applying. 5+ years of product management on infrastructure products, with hands-on exposure to 2 or more platform families: ideally one public cloud (AWS, Azure, GCP, or their managed Kubernetes services) and one on-prem (vSphere, Nutanix, bare metal). Real Kubernetes depth: you understand cluster lifecycle, upgrades, and operators well enough to earn the respect of senior engineers and enterprise platform teams. Cluster API familiarity is a strong plus. Judgment about maturity: this area includes fast-moving strategic work and stable surfaces that need discipline more than features. You can tell which is which and invest accordingly. Comfort with upstream ecosystems: you understand what tracking upstream Kubernetes costs and buys. Participation in CNCF SIGs, working groups, or open source communities is a plus, not a bar. Clear written communication across time zones and cultures. CKA/CKAD or similar hands-on Kubernetes certification is a nice signal, never a requirement. The hiring process Your time is valuable, so the process is short and each stage has a purpose: Screening interview (30 min): mutual fit and a walk through your experience. Product interview (60 min): a working session on a real problem from this domain. We're looking at how you think, scope, and decide. Technical depth interview (45 min): Kubernetes and infrastructure fluency, with one of our senior engineering leads. Culture and team interview (30 min): working in a team setting, alignment with our core values. Why this role Foundation product area with a loyal enterprise and public sector base, and a clear strategic direction into brownfield federation and AI infrastructure. Small senior team, direct access to founders, engineering leadership, and customers. Real ownership: this domain has one PM, and it's you. 2026-272

Own resolution of complex production issues and collaborate on product improvements. | 3+ years software engineering with Kubernetes internals and Go proficiency. | 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. Clarity, Precision, Documentation-Driven Engineering (DDE) • Create and maintain clear, Markdown-based RCAs, fix write-ups, and knowledge-base entries so root cause and remediation intent are documented before implementation. • Own escalations end to end — from customer symptom through code fix, backport, verification, and follow-through on preventative work. • Drive continuous improvement of the software development function through measurable reductions in escalation age, backport lead time, and repeat-defect rate. • Use sustaining KPIs such as time-to-RCA, time-to-fix, backport coverage, escaped-defect rate, and customer-confirmed resolution to guide improvements. • Collaborate effectively across Software Development, Support, Product Engineering, QA, Release Engineering, and Security teams. Minimum Qualifications • 3+years of hands-on software engineering experience Engineering, Escalation Engineering, or a comparable production-focused role. • Hands-on experience with Kubernetes - operating, debugging, and modifying Kubernetes and Kubernetes-native components (controllers, operators, CAPI, CRDs) in complex production environments. • Strong development experience - you must be comfortable reading, writing, and shipping production code, not only debugging it. • Proven experience owning high-severity customer escalations end to end, including code-level root cause and fix delivery. • Experience backporting fixes across multiple supported release branches with strong regression discipline. • Experience with debugging skills across networking, storage, and control-plane behavior. • Working knowledge of at least one major cloud platform: AWS, Azure, or GCP. • Excellent written and verbal communication — able to hold technical authority in front of customers and executives. Preferred Qualifications • Go (Golang) development experience — building, debugging, and patching Go microservices and Kubernetes-native components. • Certified Kubernetes Administrator (CKA); CKAD or CKS a plus. • Experience with Cluster API (CAPI), controller-runtime, or writing/maintaining Kubernetes operators. • Experience with edge, bare-metal, or virtualization platforms (VMware, KubeVirt, Hyper-V). • Experience delivering CVE remediation and patch releases in a regulated, enterprise SaaS, or federal environment. • Experience with MongoDB, Terraform, Ansible, or CI/CD pipelines (GitHub Actions). • Experience using AI-assisted debugging or engineering workflows with validation and governance controls.