Note: The job is a remote job and is open to candidates in USA. Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. The Site Reliability Engineer will ensure the availability, performance, and operational excellence of large-scale distributed systems by applying software engineering principles to infrastructure and operations. The role focuses on automation, observability, incident response, Kubernetes, CI/CD, capacity planning, resiliency, security, and mentoring.
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
- 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
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