I currently work in IT support, where I help people solve everyday technical issues and ensure reliable day-to-day operations.

Alongside my role, I have built hands-on experience in Linux system administration and DevOps practices, working with technologies such as Linux, Docker, Kubernetes, and cloud-native tooling.

My goal is to transition into a position in DevOps, SRE, and Linux system engineering, where I can build and operate reliable systems, automate infrastructure, and work deeply with cloud-native technologies.

• 🌡️ Cloud Telemetry Platform Designed and implemented an end-to-end telemetry platform for temperature and humidity monitoring, combining backend development, infrastructure, and observability.

  • Developed a FastAPI backend with PostgreSQL for telemetry ingestion and querying
  • Designed secure device communication using per-device API key authentication
  • Built a resilient edge service with retries, validation, and systemd hardening
  • Deployed infrastructure via GitOps (FluxCD) on Kubernetes
  • Implemented observability stack (Prometheus + Grafana) with dashboards and alerting
  • Secured private connectivity using Tailscale and managed secrets with Sealed Secrets

• ☸️ Actual Budget Kubernetes Deployment
  Built and operated a production-style Kubernetes deployment for a self-hosted finance application.

  • Deployed workloads using Kubernetes resources (Deployments, Services, CronJobs)
  • Designed secure private access using Tailscale
  • Implemented automated backups to remote storage (AWS S3)
  • Packaged and managed deployments using Helm

• 🧠 SadServers Operational Scenarios Solved real-world Linux and DevOps troubleshooting scenarios simulating production incidents.

  • Diagnosed and resolved failures across systems, networking, and services
  • Performed targeted system administration under constrained conditions
  • Strengthened debugging and incident response skills

• 🤝 Team Project: Backend & Infrastructure for RAG Pipeline Contributed to backend and infrastructure in a cross-functional team (Web, Data Science, Deep Learning).

  • Set up and maintained a Linux server environment
  • Deployed and operated a Dockerized Qdrant vector database
  • Supported infrastructure for a RAG pipeline used in ML workflows

Linux • Docker • Kubernetes • FastAPI • PostgreSQL
Prometheus • Grafana • FluxCD • Helm • Tailscale
AWS • GitOps • Systemd • Python