Junior IoT & Backend Developer (Python) based in Turin, Italy.
I build end-to-end IoT software prototypes: device/sensor data → messaging → storage → dashboards/alerts → APIs.

• Location: Turin, Italy
• Focus: IoT software, backend services, cloud integration
• Currently: Master’s student at Politecnico di Torino (Digital Skills for Sustainable Societal Transition)

I enjoy building practical systems that connect sensor data to useful outcomes:

• Collecting device events and measurements
• Handling real-time messaging
• Storing time-series data
• Monitoring with dashboards and alerting
• Exposing clean REST APIs for applications and integrations

• Linux fundamentals for running and debugging services
• Containerized development (Docker / Compose)
• Cloud IoT basics (AWS IoT Core or Azure IoT Hub)

An IoT monitoring system designed to collect, store, and visualize environmental data from multiple laboratory sensors.

• Simulated and real sensor data for temperature and humidity
• Centralized catalog service for managing devices and sensors
• Data collection through REST-based communication
• Time-series storage and historical data tracking
• Basic alerting and monitoring logic
• Modular Python codebase structured for scalability

This project helped me understand how a complete IoT monitoring pipeline works, from device registration to data storage and visualization.

Repository:
https://github.com/Aramushaa/Lab_temp_hum_monitor

An IoT prototype that detects events and supports monitoring and alerting.

• Messaging: MQTT
• Backend: Python
• Storage/Monitoring: time-series + dashboards (in progress)
• Repo: [https://github.com/Aramushaa/Iot-uni-project]

An IoT system for a tennis court using ST AIoT Craft kits to detect events and publish them for monitoring.

• Goal: detect and publish game-related events with low latency
• Planned flow: device events → MQTT → storage → dashboards/alerts
• Repo: [https://github.com/Aramushaa/smart-tennis-field]

• Languages: Python
• Messaging: MQTT (Mosquitto / EMQX basics)
• APIs: FastAPI (basic)
• Data: PostgreSQL (basic), time-series concepts (InfluxDB/TimescaleDB in progress)
• Monitoring: Grafana (basic dashboards/alerts)
• Dev tools: Git/GitHub, Docker/Compose (in progress)
• OS: Linux command line (in progress)

• Small, complete prototypes with clear architecture
• Simple setup instructions (ideally one command to run)
• Useful monitoring and alerts, not just raw data

• LinkedIn: [https://www.linkedin.com/in/[your-link]]
• Email: [your-email]