Senior Software Engineer - SRE

The Challenge

• Build and implement application observability and platform monitoring tools to continuously improve the customer experience
• Eliminate toil by automating processes, tuning alerts, and improving code where it is most needed
• Frequently evaluate new ideas and trends to identify potentially useful tools and techniques
• Collaborate with different functional groups to identify gaps, prioritize, and resolve issues
• Define, implement, and maintain Service Level Indicators (SLIs) and Service Level Objectives (SLOs) aligned with customer experience
• Design and instrument SLIs such as latency, error rates, and availability across critical services
• Manage and enforce error budgets to balance system reliability with product feature velocity
• Improve alert quality by reducing noise and focusing on actionable, high-signal alerts
• Embed with product teams to review architectures and catch reliability risks early
• Share your knowledge and experience with the Engineering organization
• Share your findings with technical leadership and senior management
• Create scripts in Python, Bash, Java, or Ruby for operational automation and incident response

Requirements

• Bachelor's degree in computer science, Engineering, or related technical or business field
• 4+ years of application development experience with Java or other equivalent language
• Experience with Spring environment
• Experience in cloud-based infrastructure (Azure, AWS, GCP, etc.)
• A knowledge of the importance of centralizing logging, metrics dashboards, and alerting
• A good awareness of databases (ideally SQL/NoSQL)
• Hands-on experience with observability tools (Datadog, Prometheus, Grafana, etc.)
• Knowledge with CI/CD pipelines and infrastructure-as-code (Terraform, Helm, Jenkins, GitLab)
• Experience with Kubernetes and container orchestration (EKS/AKS/GKE)
• Experience with distributed systems at scale
• Familiarity with service meshes and microservices architectures
• Experience with chaos engineering tools (Gremlin, Chaos Monkey)
• Background in product-facing services with high traffic scale

Qualifications

• Experience with AI-assisted incident response systems (root cause analysis, log summarization, anomaly triage)
• Experience developing or integrating Large Language Models (LLM)-based tools to reduce Mean Time To Repair (MTTR) and improve alert quality
• Experience applying machine learning techniques for anomaly detection, capacity prediction, or failure pattern analysis
• Experience deploying AI systems in production (not just experimentation)
• Knowledge with vector databases, embeddings, or Retrieval-Augmented Generation (RAG) architectures for operational intelligence
• Experience with prompt engineering and evaluating LLM outputs in the reliability workflow

Skills

• Python, Bash, Java, or Ruby
• Spring environment
• Cloud-based infrastructure (Azure, AWS, GCP, etc.)
• Observability tools (Datadog, Prometheus, Grafana, etc.)
• CI/CD pipelines and infrastructure-as-code (Terraform, Helm, Jenkins, GitLab)
• Kubernetes and container orchestration (EKS/AKS/GKE)
• Chaos engineering tools (Gremlin, Chaos Monkey)
• Product-facing services with high traffic scale
• Large Language Models (LLM)-based tools
• Machine learning techniques
• Vector databases, embeddings, or RAG architectures
• Prompt engineering and evaluating LLM outputs

Benefits

• Annual base pay range: $116,475 - $174,712.50 USD