Engage and partner with various Engineering, Operations, and Product teams to design, deliver, and maintain a highly available and performant application platform.
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Build and implement application observability and platform monitoring tools to continuously improve the customer experience
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Eliminate toil by automating processes, tuning alerts, and improving code where it is most needed
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Frequently evaluate new ideas and trends to identify potentially useful tools and techniques
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Collaborate with different functional groups to identify gaps, prioritize, and resolve issues
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Defining, implementing, and maintaining SLIs and SLOs aligned with customer experience.
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Design and instrument SLIs such as latency, error rates, and availability across critical services
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Manage and enforce error budgets to balance system reliability with product feature velocity.
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Improving alert quality by reducing noise and focusing on actionable, high-signal alerts
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Embed with product teams to review architectures and catch reliability risks early
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Share your knowledge and experience with the Engineering organization
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Share your findings with technical leadership and senior management
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Build scripts in python/bash/java or ruby for operational automation and incident response
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Bachelor's degree in computer science, Engineering, or related technical or business field
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4+ yrs. of application development experience with Java or other equivalent language
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Experience with Spring environment
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Experience in cloud-based infrastructure (Azure, AWS, GCP, etc.)
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Experience with the factors that affect software application performance at different levels. These factors include database performance, network performance, CPU utilization, JVM tuning, memory analysis, thread management, and query performance.
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A knowledge of the importance of centralizing logging, metrics dashboards, and alerting. Able to articulate about some of the tools used for these tasks
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A good awareness of databases (ideally SQL/NoSQL)
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Hands-on experience with observability tools (Datadog, Prometheus, Grafana, etc.)
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Knowledge with CI/CD pipelines and infrastructure-as-code (Terraform, Helm, jenkins, gitlab)
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Build and operate AI-assisted incident response systems (root cause analysis, log summarization, anomaly triage)
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Develop or integrate LLM-based tools to reduce MTTR and improve alert quality
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Apply machine learning techniques for anomaly detection, capacity prediction, or failure pattern analysis
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Experience deploying AI systems in production (not just experimentation)
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Knowledge with vector databases, embeddings, or RAG architectures for operational intelligence
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Well-developed insight of prompt engineering and evaluation of LLM outputs in the reliability workflow
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Kubernetes and container orchestration (EKS/AKS/GKE)
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Experience with distributed systems at scale
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Familiarity with service meshes and microservices architectures
