I run Linux servers across both Microsoft Azure and Amazon Web Services (AWS) and require a practical, production-ready solution to detect operational and security issues before they escalate into incidents. The objective of this project is to design and implement a Python-based cloud monitoring and anomaly detection system that continuously observes system metrics, logs, and basic network signals, learns what “normal” behavior looks like over time, and flags deviations that may indicate early-stage security or stability problems. This system is intended for real operational environments, not academic experimentation or proof-of-concept tooling. It must be reliable, maintainable, auditable, and suitable for long-term use in cloud infrastructure without reliance on a full Security Operations Center. The solution must be capable of identifying early indicators of risk, including but not limited to abnormal CPU or memory consumption patterns, suspicious or unexpected network activity, brute-force login attempts, and anomalous authentication behavior that may precede compromise or service disruption. When an anomaly is detected, the system must immediately generate actionable alerts by writing structured entries to local log files and sending email notifications in parallel, enabling rapid human response. Alerting logic must be deterministic, explainable, and designed to minimize false positives. A lightweight, web-based dashboard is required to provide at-a-glance visibility into current system health, baseline status, and recent alerts. The dashboard is intended for administrators who do not operate a dedicated SOC and must therefore be clear, concise, and operationally useful. Deployment must be simple and flexible, supporting either a single-host installation or container-based deployment. The solution should be suitable for modest cloud instances and avoid unnecessary dependencies or excessive resource consumption. The same architecture should be deployable across Azure and AWS environments with minimal configuration changes. Technology choices are flexible, provided the implementation remains open, auditable, and easy to maintain. The core system is expected to be written in Python and may leverage standard libraries and lightweight data analysis or machine-learning techniques where appropriate, provided the behavior of the system remains transparent and controllable. Clear and professional documentation is required as part of the deliverables. This includes installation instructions, configuration options, usage examples, operational guidance, and a description of known limitations. Documentation must be sufficient for a junior-to-mid-level administrator to deploy, operate, and troubleshoot the system without external assistance. This engagement represents a foundational implementation with a real market value significantly higher than the posted budget, and is intentionally structured to allow for future expansion, additional detection capabilities, and phased enhancements based on performance and results. Due to compliance with United States export control, financial, and data protection regulations, we are only considering candidates based in countries aligned with U.S. regulatory frameworks. Candidates must be able to operate under U.S.-aligned compliance standards, including eligibility with respect to economic sanctions, identity and financial verification requirements, and adherence to U.S.-based data protection and privacy expectations. Applicants must confirm their eligibility to work under these conditions in order to be considered for this engagement.