DevOps & SRE Engineer

Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We are seeking an experienced DevOps & SRE Engineer to ensure the availability, performance, and operational excellence of large-scale distributed systems in production, applying strong software engineering principles to infrastructure and operations problems.

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

Requirements:

• Bachelor's degree in Computer Science, Engineering, or a related technical discipline
• Five 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