• Lead/Manage/Support the development and implementation of automated compliance and security monitoring solutions using platforms such as Qmulos • Collaborate with stakeholders and cybersecurity operations, engineering, and compliance teams to implement automated security monitoring and continuous compliance capabilities • Drive the development and maturation of automated compliance monitoring and security orchestration capabilities to improve operational efficiency and visibility • Utilize platforms such as Qmulos and related security automation tools to support continuous monitoring and compliance validation

• Design and provision standardized, scalable virtual environments using VMware, Packer, and Ansible. • Deploy and manage containerized ecosystems using Docker and Kubernetes, leveraging Helm to keep our deployments clean and consistent. • Build and refine high-performance CI/CD pipelines in Jenkins, ensuring every change is version-controlled through rigorous GitOps methodologies. • Develop maintainable, robust tools using Python and PowerShell/PowerCLI to integrate disparate systems and eliminate manual intervention. • Act as a senior-level escalation point for both Linux and Windows administrative challenges. • Assist in the cutting-edge automation and monitoring of our enterprise VoIP and SIP infrastructure.

• Engineer, automate, and continuously optimize audit workflows by integrating Generative AI, agentic orchestration, and process engineering principles. • Audit Architecture & Process Re-Engineering: Analyze and map complex end-to-end audit processes to identify 'automation-first' opportunities. • Lean Audit Design: Apply Lean/Six Sigma principles to eliminate 'waste' in the audit lifecycle. • Design standardized, modular workflow patterns that can be reused across audit domains. • Build and deploy AI-assisted workflows to automate control mapping and summarize meeting transcripts. • Ensure all AI solutions are 'explainable' and meet corporate standards. • Design and maintain Power BI or Tableau visualizations that provide a live 'Risk Heatmap'.

• Design and implement production-ready tools that integrate LLMs and other automation techniques into research workflows. • Write modular, testable, maintainable Python code grounded in strong engineering principles. • Build reproducible pipelines that transform raw data into structured, analysis-ready outputs, with validation and logging built in. • Develop services and integrations that connect survey platforms, databases, and AI components. • Implement structured testing and evaluation frameworks to validate automated outputs and preserve methodological rigor. • Build lightweight tools or interfaces that enable researchers to interact seamlessly with automated systems.