Note: The job is a remote job and is open to candidates in USA. Bright Vision Technologies is seeking a Site Reliability Engineer to ensure the availability, performance, and operational excellence of large-scale distributed systems in production. The role focuses on reliability engineering, incident response, observability, automation, and resilient platform operations.
Responsibilities
- Define, instrument, and continually refine service-level objectives (SLOs), service-level indicators (SLIs), and error budgets for critical services, and use those measures to drive concrete engineering and prioritization decisions
- Lead incident response and resolution for production issues, acting as a calm and effective incident commander when needed, and ensuring high-quality post-incident reviews that drive lasting improvements
- Design and implement comprehensive monitoring, logging, and tracing strategies using Prometheus, Grafana, OpenTelemetry, ELK/EFK, Datadog, or similar tooling so that operators have rich, actionable visibility into system behavior
- Build and maintain robust on-call processes, runbooks, and escalation paths that reduce mean time to detect and mean time to resolve while protecting the well-being of the engineers on rotation
- Automate operational toil aggressively by writing production-grade tooling in Python, Go, Bash, or similar languages, replacing manual workflows with reliable, auditable automation
- Architect and operate large-scale Kubernetes clusters and container-based workloads, including autoscaling, capacity planning, network policy, and integration with service meshes
- Design CI/CD pipelines that promote safe, frequent, and observable releases, supported by automated testing, canary deployments, feature flags, and progressive rollout strategies
- Lead capacity planning and performance engineering activities, building models that predict growth and stress, and validating those models through load testing and chaos experiments
- Partner closely with application development teams to embed reliability practices early in design — including failure-mode analyses, graceful degradation patterns, and dependency hardening
- Strengthen the platform’s resiliency through chaos engineering, fault injection, dependency isolation, retries, timeouts, circuit breakers, and well-tested failover paths
- Drive continuous improvement of security posture in collaboration with security teams, including patch management, vulnerability remediation, and secure-by-default platform defaults
- Contribute to the technical roadmap for reliability tooling, observability platforms, and developer-experience improvements that reduce friction and improve outcomes for engineering teams
- Mentor engineers across the organization on SRE practices and foster a strong, blameless culture of operational excellence
Skills
- U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply
- We are unable to sponsor new H-1B visa petitions for this position
- Bachelor's degree in Computer Science, Engineering, or a related technical discipline
- Ten or more years of SRE, DevOps, or production engineering experience supporting large-scale distributed systems
- Strong programming skills in at least one of Python, Go, or Java, with the ability to build robust automation and tooling
- Deep, hands-on experience operating Linux at scale, including networking, performance tuning, and systems-level troubleshooting
- Production experience operating Kubernetes and container-based workloads
- Strong working knowledge of observability tooling such as Prometheus, Grafana, OpenTelemetry, ELK/EFK, or commercial equivalents
- Hands-on experience designing and operating CI/CD pipelines for both infrastructure and applications
- Solid understanding of distributed system design, including consistency models, partitioning, and failure semantics
- Demonstrated experience leading incident response and conducting effective post-incident reviews
- Excellent communication and documentation skills
- Experience defining and operationalizing SLOs and error budgets in real production environments
- Exposure to chaos engineering practices and tools such as Chaos Monkey, Gremlin, or Litmus
- Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP)
- Background in capacity planning, performance engineering, or large-scale load testing
- Familiarity with service mesh technologies such as Istio, Linkerd, or Consul
Benefits
- 100% Remote (U.S.)
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