Senior Site Reliability Engineer

Spectro Cloud•San Jose, CA

About The Position

Spectro Cloud is seeking a hands-on Site Reliability Engineer to advance the resilience and operational excellence of its US- and EU-based global Palette platform. The role operates and improves production systems across commercial and federal deployments, contributing to service-level objectives, incident response, production readiness, observability, automation, disaster recovery, and performance optimization. The engineer will build, operate, and continuously improve a declarative, self-healing, cloud-native Kubernetes platform. Work includes Go-based microservices; production-ready CI/CD pipelines using GitHub Actions; infrastructure automation with Terraform and Ansible; unified observability; and MongoDB and distributed-systems operations. The role partners with Platform Engineering, DevOps, and product engineering to reduce toil, improve recovery speed, and prevent repeat incidents. The position calls for a proactive builder-and-operator mindset, sound troubleshooting of complex distributed systems, disciplined reliability engineering, and responsible use of AI with human validation and attention to security and engineering accountability. Spectro Cloud’s Engineering organization emphasizes clarity, precision, documentation-driven engineering, validation-first execution, and ownership of services and reliability outcomes.

Requirements

At least 5 years of hands-on experience in Site Reliability Engineering, Production Engineering, DevOps, Platform Engineering, or operating high-scale distributed systems.
Strong hands-on experience operating, scaling, and troubleshooting Kubernetes in complex production environments.
Programming proficiency in Python, Go, Java, or a comparable language, with a focus on automation and operational tooling.
Hands-on experience with observability systems spanning metrics, logs, traces, alerting, dashboards, and incident response/on-call operations.
Strong Infrastructure-as-Code experience with Terraform and/or Ansible.
Operational experience with at least one major cloud platform: AWS, Azure, or GCP.
Experience troubleshooting distributed systems, networked services, and production performance issues using data-driven methods.
Working knowledge of MongoDB operations, query/index performance, and high-availability concepts in production environments.

Nice To Haves

Experience with Go-based microservices and REST APIs.
Experience designing or operating production systems for enterprise SaaS, regulated, or federal environments.
Experience implementing disaster-recovery, capacity-planning, or load-testing programs.
Experience using AI-assisted engineering or operations workflows with strong validation and governance controls.

Responsibilities

Implement and improve practices for production stability, performance, scalability, observability, and disaster recovery.
Define, instrument, monitor, and improve SLIs, SLOs, and error budgets in partnership with service owners.
Participate in incident response, including on-call, mitigation, blameless root cause analysis, and long-term preventative remediation.
Identify recurring reliability risks and drive engineering changes that improve production resilience.
Build, operate, and improve the observability stack using technologies such as Prometheus/Grafana, ELK/Loki, and distributed tracing.
Create dashboards, alerts, and runbooks to provide visibility into system health and user experience.
Perform performance tuning, capacity planning, and load testing to meet demand and SLOs.
Reduce alert noise and improve signal quality for faster incident detection and resolution.
Implement safe deployment strategies such as canary releases, rolling updates, blue/green deployments, and feature flags.
Build automated rollback and recovery mechanisms to minimize MTTR.
Automate repetitive operational work and reduce toil through tooling, standardization, and self-service capabilities.
Promote production readiness, clear service ownership, and a 'you build it, you run it' mindset.
Use generative AI responsibly for repeatable SRE tasks, including RCA summarization, runbook generation, operational analysis, and incident-pattern identification; validate AI-generated artifacts before use.
Contribute to the evolution of a scalable, resilient Kubernetes control plane, reducing failure domains and operational complexity.
Design and improve resilient Go microservices and REST APIs with reliability, security, and performance principles.
Troubleshoot and optimize MongoDB performance, indexing, and distributed-systems behavior to support high availability and data integrity.
Partner with engineering teams on production-readiness reviews and reliability improvements before and after service launches.
Create and maintain Markdown-based design specifications, operational documentation, and runbooks.
Own services and reliability outcomes end to end, including operational readiness, on-call participation, and remediation follow-through.
Use reliability KPIs such as MTBF, MTTR, deployment success rates, SLO attainment, and error-budget consumption to guide improvements.
Collaborate across SRE, Platform Engineering, DevOps, Security, and product engineering teams.