Senior Site Reliability Engineer

This is a fully remote position, open to applicants in Poland.

📋 Description

• Ensure the availability, reliability, and performance of high-traffic Java-based applications in distributed environments.

• Troubleshoot and resolve complex issues in both production and non-production environments.

• Engage in pre- and post-deployment performance testing and monitoring activities.

• Enhance Java application performance through JVM tuning, efficient resource management, and horizontal scaling techniques.

• Deploy and manage monitoring, logging, and alerting tools such as Grafana, Prometheus, Loki, Mimir, and Alloy.

• Implement observability strategies while maintaining dashboards, alerts, and log queries.

• Integrate AI/ML models for tasks such as anomaly detection, predictive alerting, intelligent alert correlation, and noise reduction.

• Design, construct, and operate agentic AI workflows for alert triage, root cause analysis, runbook execution, and incident summarization.

• Develop tool-calling LLM agents that interact with Kubernetes, Grafana, Jira, Slack, and PagerDuty APIs.

• Build and maintain MCP servers and integrations that expose internal systems to AI agents.

• Evaluate and operationalize LLM frameworks and orchestration platforms.

• Implement guardrails, evaluation harnesses, and feedback loops to ensure the safety and accuracy of AI agents.

• Advocate for AI-assisted development and operational practices.

• Support incident response efforts, conduct post-mortem analyses, and identify root causes.

• Utilize AI tools to expedite incident resolution timelines and create drafts for post-mortem reports.

• Document and disseminate lessons learned.

• Identify repetitive workflows and develop AI-augmented or automated alternatives.

• Create self-service tools and chatbot interfaces for system status inquiries, log retrieval, and SOP execution.

• Measure and report on metrics related to toil reduction.

• Collaborate effectively with developers, architects, data/ML engineers, DevOps, and NOC teams.

• Communicate SRE practices, AI/automation capabilities, and operational insights to both technical and non-technical stakeholders.

• Provide feedback regarding application performance and observability metrics.

⛳️ Requirements

• A degree in Computer Science or a related field, or equivalent professional experience.

• Over 5 years of experience in SRE, DevOps, or comparable infrastructure roles, with a focus on managing large-scale, high-availability production systems.

• At least 3 years of hands-on experience managing production Kubernetes clusters, covering architecture, networking, storage, and security aspects.

• Familiarity with cluster autoscaling (Karpenter), upgrades, and multi-cluster management.

• Proficiency in using kubectl, Helm, Kubernetes operators, and troubleshooting container orchestration issues.

• Advanced knowledge of the Grafana observability stack, including dashboards, alerting, visualization, and Grafana Alloy.

• Proficiency in PromQL and experience with Loki for log aggregation and analysis.

• Hands-on experience managing Java-based applications in distributed systems, including JVM tuning and optimization.

• Expertise in cloud platforms, preferably AWS, with GCP or Azure also being valuable.

• Familiarity with Terraform/Terragrunt or Ansible.

• Proficient in ArgoCD for GitOps workflows and continuous deployment processes.

• Strong scripting skills in Python, Bash, or Go, with experience in building CI/CD pipelines and deployment automation.

• Demonstrated experience with on-call rotations, incident response, and root cause analysis.

• At least 1 year of practical experience in building or operating AI/LLM-powered tools, agents, or workflows in production or similar environments.

• Capability to design agentic systems utilizing tool calling, RAG, or multi-step reasoning.

• Experience with integrating LLM APIs such as Anthropic Claude, OpenAI, or open-source models into backend services or automation pipelines.

• Familiarity with at least one agentic orchestration framework or workflow engine, such as LangChain, LangGraph, CrewAI, n8n, Temporal, or equivalent.

• Understanding of prompt engineering best practices, including structured outputs, system prompts, and few-shot examples.

• Knowledge of AI-assisted coding tools such as Claude Code, Codex, and Cursor.

• Experience in building or utilizing MCP servers.

• Awareness of AI safety, hallucination mitigation, and human-in-the-loop design patterns.

• Strong written and verbal communication skills.

• Ability to convey complex AI and infrastructure concepts to a diverse audience.

• Capacity to mentor junior team members in traditional SRE practices and emerging AI-driven methodologies.

• A positive attitude and receptivity to constructive feedback.

🏝️ Benefits

• The autonomy to explore and experiment with cutting-edge AI tools.

• Support from leadership to implement AI tools in production environments.

• A mandate to significantly reduce operational toil through intelligent systems.