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 Platform Reliability Engineer will ensure the availability, performance, and operational excellence of large-scale distributed systems in production by applying software engineering principles to infrastructure and operations, automating complex services, and reducing operational toil.

Responsibilities

• Ensure the availability, performance, and operational excellence of large-scale distributed systems in production
• As an SRE you will live at the boundary between development and operations, applying strong software engineering principles to infrastructure and operations problems, and continually pushing the platform toward higher reliability with lower operational toil
• The ideal candidate will combine deep systems knowledge with strong programming skills, a measurement-driven mindset, and the discipline to design, automate, and operate complex services so that reliability becomes a first-class engineering deliverable rather than a reactive concern

Skills

• 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

Benefits

• 100% Remote (U.S.)
• Full-time, Direct W2

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