Description AI-Driven Incident Remediation (LARS) Design and implement new LARS remediation workflows across single-tenant (ST) and multi-tenant (MT) OAC instances. Expand automated coverage for service health, capacity, cluster availability, and network-related alarms. Enhance AI-driven diagnostics, triage, pattern detection, and auto-approval pipelines for incident mitigation. Improve observability, cross-pod dashboarding, and multi-pod coordinated incident support. AI Initiatives for OASE DevOps Develop and integrate AI-assisted diagnostics and automated mitigation for high-severity production incidents. Contribute to Agentic DevOps initiatives, including autonomous remediation frameworks and prototype agent workflows. Collaborate with ML teams to incorporate models for anomaly detection, root-cause analysis, and remediation recommendations. AI assisted Automated Change Management Build tooling and CI/CD pipeline extensions to eliminate manual change processes and streamline deployment safety. Design guardrails, approval workflows, and automated rollouts to improve release reliability and reduce operational toil. Agentic DevOps Platform (MCP Servers) Develop MCP servers and agent orchestration workflows enabling end-to-end automated diagnostics and incident resolution. Integrate agent-driven actions with existing automation systems (LARS, CI/CD, service health signals). Contribute to next-generation self-healing and autonomous operations capabilities across OAC services.