Eshaal K.

Developed AI-driven monitoring systems to proactively identify and address issues in multi-cloud and hybrid environments. Utilized machine learning algorithms to predict system performance and detect real-time anomalies. Enhanced event management workflows through automation of incident identification and response, collaborating with DevOps teams to integrate AI-based tools into existing CI/CD pipelines. Configured and maintained AIOps platforms such as Moogsoft, BigPanda, and Splunk to streamline operations. Created and refined predictive models analyzing historical data for forecasting system failures and performance degradation. Automated incident triage processes to improve mean time to resolution (MTTR) and implemented root cause analysis capabilities with machine learning to facilitate problem resolution. Integrated monitoring tools with AIOps solutions, enhancing observability by collecting and correlating logs, metrics, and traces. Advanced security monitoring using AI for potential cybersecurity threat detection. Managed scalability of AI models to accommodate high volumes of telemetry data while ensuring optimal performance. Ensured data governance and compliance during AI solution deployment across operational environments. Collaborated with cross-functional teams to implement AI-driven solutions in IT operational areas including infrastructure, network, and application performance monitoring. Conducted testing and validation of AI models to guarantee accuracy in identifying operational events and improved alerting systems by prioritizing actionable alerts. Provided technical leadership in the implementation of AI techniques for real-time monitoring and performance optimization, while remaining current with industry advancements in AIOps and artificial intelligence.