Production infrastructure, CI/CD automation & high-reliability systems in the synthetic grid.
• Built a shift-left CI/CD pipeline running Semgrep static analysis and TruffleHog secret scanning on every push, with deployment gates on critical findings.
• Piped scan results to an LLM via API for contextual vulnerability analysis and auto-generated pull requests with remediation code, reducing manual triage time.
• Architected a distributed load-testing system with a FastAPI orchestrator, Redis Streams-based job queue, and containerized asyncio/httpx worker pods deployed on Kubernetes.
• Designed the full system for horizontal scale, including a custom K8s autoscaler and Prometheus/Grafana metrics pipeline, backed by PostgreSQL for test configs and results.
| Period | Degree / Specialization | Institution |
|---|---|---|
| Present | M.Sc. Cyber Security with Artificial Intelligence | University of Lancashire (UK, Remote) |
| Completed | B.Sc. Computer Science | Memorial University of Newfoundland (Canada) |
• 8-Week course covering LLM threat modeling, prompt injection defense, AI agent hardening, CI/CD-based
vulnerability remediation, and using LLMs for log analysis.
• Capstone: Secured a Django application with LLM integration by identifying and fixing AI-specific risks
and traditional vulnerabilities improving overall system reliability and protecting sensitive data.
• 2-Months comprehensive training in Python, Probability & Statistics, Deep Learning, and NLP.
• Capstone: Developed a telecom churn prediction system to analyze customer retention, deploying the
solution via Streamlit.