DevOps & SRE Engineer specializing in infrastructure reliability, incident management, and self-healing systems. Building AI-powered platforms for automated incident detection, diagnosis, and controlled remediation. Currently a master's student in International Software Systems Science at the University of Bamberg.

An AI-powered platform for automated incident detection, diagnosis, controlled remediation, and recovery verification. Implements a complete 9-state incident lifecycle with evidence collection, deterministic diagnosis, optional AI/RAG-enhanced root cause analysis, allowlisted remediation with risk-based approval workflow, and automatic recovery verification.

• Self-Healing Pipeline: Detection → Incident → Evidence → Diagnosis → Remediation → Verification → Resolution
• Key Features: HTTP health & Prometheus detectors, ChromaDB runbook retrieval, SLO/SLI monitoring (MTTD, MTTR), cost optimization, rate limiting, RBAC, audit logging
• Infrastructure: Docker Compose, Kubernetes/Helm, Terraform, Argo CD GitOps, GitHub Actions CI/CD with Trivy/Gitleaks security scanning
• Testing: 39 tests (unit, integration, end-to-end)
• Tools: Python • FastAPI • PostgreSQL • Redis • Prometheus • Grafana • ChromaDB • Docker • Kubernetes • Helm • Terraform • Argo CD • GitHub Actions

An ML microservice for real-time and batch customer-churn prediction, with feature explanations and data-drift monitoring.

• Tools: Python • Scikit-Learn • FastAPI • Streamlit • Evidently AI • Docker

A backend data pipeline for ETL processing, data validation, database integration, and analytics APIs.

• Tools: Python • Pandas • SQLAlchemy • PostgreSQL • FastAPI

• DevOps & Infrastructure Automation
• Cloud Engineering & Platform Reliability
• Kubernetes & Container Orchestration
• CI/CD Pipelines & Infrastructure as Code
• System Observability & Monitoring
• AI-Assisted Operations & Self-Healing Systems
• SRE Practices: SLO/SLI, Incident Management, Root Cause Analysis