• Design, develop, and maintain AI/ML models for anomaly detection, trend analysis, and signal correlation across metrics, logs, traces, and events. • Reduce alert noise through intelligent alert grouping, suppression, and prioritization. • Enhance observability platforms with AI-generated insights supporting SLO and error-budget management. • Implement AI-driven incident classification, enrichment, and summarization. • Provide probable root-cause analysis recommendations based on historical and real-time telemetry. • Support on-call and incident response teams with AI-guided remediation suggestions. • Contribute AI insights to post-incident reviews and reliability improvement plans. • Apply AI techniques to identify repetitive operational tasks and automation opportunities. • Assist in generating, validating, and optimizing automation playbooks and workflows. • Analyze automation execution data to improve success rates, resiliency, and reuse. • Build and maintain AI-searchable knowledge repositories containing runbooks, SOPs, lessons learned, and historical incident data. • Enable natural-language access to operational knowledge for SREs and operations staff. • Develop predictive models for capacity planning, failure forecasting, configuration risk, and reliability debt identification. • Support proactive remediation strategies to prevent incidents before customer impact. • Assist SRE leadership in data-driven prioritization of reliability investments. • Ensure AI solutions adhere to organizational security, compliance, and data-handling policies. • Establish guardrails for AI recommendations and automation execution. • Promote transparency, explainability, and auditability of AI-driven operational decisions.