Senior Site Reliability Engineer

Role Overview

• Ensure the availability, reliability, and performance of high-traffic Java-based applications in a distributed environment.
• Troubleshoot and resolve complex issues across production and non-production environments.
• Participate in pre
• and post-deployment performance testing and monitoring to continuously improve application performance.
• Optimize Java application performance with a focus on JVM tuning, efficient resource utilization, and horizontal scaling.
• Deploy and manage the Grafana stack (Grafana, Prometheus, Loki, Mimir, Alloy) to deliver real-time monitoring, logging, and alerting.
• Implement and refine observability strategies that enhance visibility into application and infrastructure health.
• Create and maintain dashboards, alerts, and log queries for comprehensive system health monitoring.
• Integrate AI/ML models into the observability pipeline for anomaly detection, predictive alerting, and intelligent alert correlation and noise reduction.
• Design, build, and operate agentic AI workflows that automate operational tasks such as alert triage, root cause analysis, runbook execution, and incident summarization.
• Develop tool-calling LLM agents that interact with infrastructure APIs (Kubernetes, Grafana, Jira, Slack, PagerDuty) to execute diagnostic and remediation actions autonomously or with human-in-the-loop approval.
• Build and maintain MCP (Model Context Protocol) servers and integrations that expose internal systems as tool surfaces for AI agents.
• Evaluate, select, and operationalize LLM frameworks and orchestration platforms (e.g., LangChain, LangGraph, CrewAI, n8n, or custom solutions) for production-grade agentic systems.
• Implement guardrails, evaluation harnesses, and feedback loops to ensure AI agent outputs are accurate, safe, and continuously improving.
• Champion the adoption of AI-assisted development and operations practices across the SRE and broader engineering organization.
• Support the operations team’s incident response efforts, conduct post-mortems, and identify root causes to prevent recurrence.
• Leverage AI tools to accelerate incident timelines, auto-generate post-mortem drafts, and surface patterns across historical incidents.
• Document and share lessons learned, contributing to a culture of continuous improvement.
• Identify repetitive operational workflows and engineer AI-augmented or fully automated replacements.
• Build self-service tools and chatbot interfaces that allow engineering teams to query system status, retrieve logs, and execute standard operating procedures through natural language.
• Measure and report on toil reduction metrics to quantify the impact of automation initiatives.
• Work closely with developers, architects, and data/ML engineers to design solutions that improve reliability and leverage AI capabilities.
• Collaborate with DevOps and NOC teams to support the application platform.
• Communicate SRE practices, AI/automation capabilities, and operational insights to technical and non-technical stakeholders.
• Provide feedback on application performance, potential improvements, and observability metrics.

Requirements

• Degree in Computer Science or a related field, or equivalent professional experience.
• 5+ years in SRE, DevOps, or similar infrastructure roles with experience managing large-scale, high-availability production systems.
• 3+ years hands-on experience managing production Kubernetes clusters, including deep understanding of architecture, networking, storage, and security.
• Experience with cluster autoscaling (Karpenter), upgrades, and multi-cluster management.
• Proficiency with kubectl, Helm, Kubernetes operators, and container orchestration troubleshooting.
• Advanced expertise with the Grafana observability stack: dashboards, alerting, visualization, and Grafana Alloy for telemetry collection.
• Proficiency in PromQL and experience with Loki for log aggregation and analysis.
• Hands-on experience managing Java-based applications in distributed environments, including JVM tuning and optimization.
• Cloud platform expertise (AWS preferred; GCP or Azure also valued).
• Familiarity with Infrastructure as Code tools such as Terraform/Terragrunt or Ansible.
• ArgoCD proficiency for GitOps workflows and continuous deployment.
• Strong scripting abilities in Python, Bash, or Go, with experience building CI/CD pipelines and deployment automation.
• Proven track record with on-call rotations, incident response, and root cause analysis.
• 1+ years of practical experience building or operating AI/LLM-powered tools, agents, or workflows in a production or production-adjacent context.
• Demonstrated ability to design agentic systems that use tool calling, retrieval-augmented generation (RAG), or multi-step reasoning to accomplish operational tasks.
• Experience integrating LLM APIs (e.g., Anthropic Claude, OpenAI, or open-source models) into backend services or automation pipelines.
• Familiarity with at least one agentic orchestration framework or workflow engine (LangChain, LangGraph, CrewAI, n8n, Temporal, or equivalent).
• Understanding of prompt engineering best practices, including structured outputs, system prompts, and few-shot examples.
• Familiarity with AI-assisted coding tools (Claude Code, Codex, Cursor) and their integration into engineering workflows.
• Experience building or consuming MCP (Model Context Protocol) servers to expose internal tools to AI agents.
• Awareness of AI safety, hallucination mitigation, and human-in-the-loop design patterns for autonomous systems.

Tech Stack

• Ansible
• AWS
• Azure
• Cloud
• Google Cloud Platform
• Grafana
• Java
• Kubernetes
• Prometheus
• Python
• Terraform
• Go

Benefits

• Competitive pay and benefits
• Flexible vacation allowance
• A hybrid / remote working environment
• Startup culture backed by a secure, global brand