Built an AI-assisted infrastructure monitoring and observability platform designed to simulate production-style telemetry analysis, anomaly detection, and automated incident workflows. Developed backend services using Python, Flask, REST APIs, PostgreSQL/MongoDB, and Docker to process and analyze 100+ system metrics per minute across monitoring pipelines, diagnostics flows, and health-check systems. Designed real-time monitoring workflows with automated alerting, threshold-based anomaly detection, logging pipelines, and observability dashboards to improve incident visibility and reduce simulated detection latency by ~40%. Engineered modular APIs and scalable backend architecture supporting telemetry ingestion, diagnostics tracking, system health analysis, and automation-ready remediation workflows. Built frontend dashboard systems for infrastructure visualization, monitoring insights, and operational analytics with emphasis on reliability, debugging workflows, and production-style system behavior. Focused heavily on backend engineering, monitoring systems, observability, automation pipelines, CI/CD practices, and deployment consistency using Render, Docker, and Git-based workflows.