Infrastructure Engineer
5 years operating enterprise virtualization and VDI platforms, with hands-on experience across Azure, AWS, and Terraform-driven infrastructure automation.
Infrastructure Engineer with 5 years of enterprise experience operating and supporting mission-critical virtualization and VDI platforms, including Citrix Cloud, VMware vSphere, and Microsoft Hyper-V, alongside Azure and AWS cloud infrastructure. Led on-premises to Azure cloud migrations with minimal downtime, and brings hands-on Windows Server administration, Active Directory, FSLogix, and storage/policy management.
Directly experienced in infrastructure-level vulnerability remediation using Qualys — managing vulnerability remediation across an estate of approximately 2,000 Citrix Virtual Desktops and 200 XenServer-hosted application servers, fetching and segregating scan reports, and coordinating monthly patching and secure configuration changes to bring platforms into compliance.
Automates operational tasks using Python — including VM and virtual desktop provisioning — and applied hands-on Terraform and GitHub Actions CI/CD in an MSc dissertation project building an ML-based infrastructure misconfiguration scanner. Experienced in incident response, on-call support for high-priority escalations, and infrastructure health monitoring via ServiceNow and Power BI, with a track record of authoring SOPs, runbooks, and knowledge base articles that support reliable, well-governed, operationally mature platforms.
Comfortable collaborating with infrastructure, security, and application teams to drive platform reliability through patching, proactive monitoring, and continuous service improvement.
AI-Driven Terraform Misconfiguration Scanner
MSc Dissertation · University of East London
Trained and compared Logistic Regression, Random Forest, and XGBoost classifiers on 480 real-world Terraform files spanning six AWS resource types to detect misconfigurations from TF-IDF text features. XGBoost outperformed a rule-based baseline, confirmed via 5-fold cross-validation (mean 95.43%). A second classifier assigns graded LOW / MEDIUM / HIGH severity scores at 94.57% accuracy.
Packaged both models into a scanner deployed inside a GitHub Actions CI/CD pipeline that automatically scans pull requests touching Terraform files and blocks merges on detected HIGH-severity issues — validated against a real pull request containing a deliberately introduced misconfiguration.
Citrix Cloud, MCS/PVS, XenApp/XenDesktop, App-V, VMware vSphere, Microsoft Hyper-V
Azure, AWS (EC2, S3, IAM, Lambda), Terraform, GitHub Actions CI/CD, Python, scikit-learn/XGBoost
Windows Server, Active Directory, FSLogix, ServiceNow, Power BI, ITIL (CAB/TAB)
Qualys vulnerability management, infrastructure-level remediation, network security fundamentals