Sr. Backend Engineer (AI Threat Analysis Platform)

• →Own the production operation of the platform end to end, including reliability, availability, performance, and cost
• →Define SLOs and build monitoring, alerting, and incident response processes; lead on-call response and blameless postmortems
• →Architect and evolve scalable backend systems that orchestrate multi-stage analysis workloads, including AI-driven components, and process results at scale
• →Build and maintain CI/CD pipelines and Infrastructure as Code to enable safe, repeatable releases and rollbacks
• →Monitor and optimize per-request cost across compute and AI inference; build cost visibility and anomaly detection
• →Establish operational excellence practices: runbooks, capacity planning, load testing, and on-call rotation design that scales beyond a single owner
• →Drive technical decisions on system architecture, technology stack, and infrastructure design
• →Champion AI adoption to enhance development workflows and code quality

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related field
• 5+ years of backend development experience delivering and operating production systems at scale
• Hands-on experience owning production services, including on-call and incident handling through root cause analysis and follow-up actions
• Proficiency in at least one backend language
• Hands-on experience with microservices development and Kubernetes deployment
• Experience designing and managing CI/CD pipelines
• Cloud-based development experience (AWS preferred; Azure or GCP acceptable)
• Proficiency in Infrastructure as Code frameworks (Terraform, CloudFormation, or Pulumi)
• Experience designing observability (metrics, logs, tracing) and defining alerts, not only consuming existing dashboards

• Experience operating services that depend on AI inference or external APIs (rate limits, retries, cost control), including LLM gateway, tracing, and evaluation tooling integrated into CI/CD pipelines
• Experience with workload isolation or secure execution environments
• Experience with queue-based or asynchronous processing systems at scale (Kafka, SQS, or similar)
• Experience with resource estimation, capacity planning, and cost optimization in the cloud
• Ability to drive cross-team adoption of operational practices such as on-call, SLOs, and postmortems
• Expertise in containerization (Docker) and DevOps/SRE practices
• Proficiency with AI development tools (Claude Code, Cursor)
• Experience in security product or cybersecurity domain

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連結智慧 守護世界 --- Connected Intelligence for Securing a Connected World