Utilized Microsoft Azure DevOps Webhooks and Microsoft Teams API to connect to company SQL Database and create custom .NET integrations between Microsoft products and financial systems.• Optimized a rider application by developing backend services with ASP.NET, integrating Docker for containerization, and implementing GitHub Actions for CI/CD, which streamlined deployments, cutting deployment times by 50%.• Enhanced microservices scalability by 30% in a transportation management system by automating configuration with Ansible, and utilizing AWS Lambda for serverless processing, resulting in quicker response times and improved system reliability.• Streamlined the deployment of a data analytics platform by deploying MongoDB clusters with Kubernetes and implementing continuous monitoring with Prometheus and Grafana, leading to a 25% reduction in query response times.

Implemented a predictive maintenance system using Python (TensorFlow) for machine learning models, C# for integration into fleet management, SQL Server for data storage, and AWS SageMaker for training and deployment, reducing vehicle downtime by 40% and maintenance costs by 20%.• Developed a real-time traffic monitoring system using ASP.NET for backend APIs and utilizing React for the user interface, integrating 500+ IoT sensors through AWS IoT Core, reducing traffic congestion by 30%.• Engineered an Automated Incident Response System with Python (FastAPI) and Vue.js, utilizing MongoDB for real-time log management, Jenkins CI/CD pipelines for deployment, and Prometheus and AWS CloudWatch for monitoring, achieving a 35% reduction in resolution time by enabling rapid incident identification and mitigation.• Designed and implemented an API Gateway in C# for real-time requests from external security tools, deployed via Azure API Management, optimizing data flow, improving request throughput by 30%, and ensuring secure access control for clients.

• Utilized Palo Alto Networks firewalls, Snort IDS, and Splunk SIEM to proactively monitor and maintain security controls, leading to a 35% reduction in incident response time and a 20% increase in detection accuracy, resulting in enhanced overall security posture.• Configured and deployed Cisco routers, integrating them into segmented VLANs, which optimized network traffic and reduced latency by 25%, improving overall network performance.• Administered and monitored 50+ Linux and Windows servers using Nagios and Ansible, leading to a 40% reduction in downtime and ensuring 99.9% server uptime through proactive maintenance and automated patch management.• Developed Python automation scripts for log file analysis and data extraction, reducing manual processing time by 60%, which accelerated the deployment of security updates and saved approximately 15 hours of labor per week.

Developed and validated predictive models using machine learning libraries such as TensorFlow and Scikit-Learn to analyze cybersecurity threats and vulnerabilities.• Enhanced a data analysis platform for a project on real-time environmental monitoring, integrating new features to support advanced statistical models and improve data visualization, thereby increasing tool accuracy and usability.• Conducted performance testing on a climate data analysis tool, resolving bottlenecks and improving efficiency by 40% and responsiveness by 30%.• Developed and fine-tuned C++ algorithms for simulating complex hardware interactions, enhancing simulation accuracy and reducing execution time in a computer engineering research project.