Tehila H.

Responsibilities:

• Developed a Python-based simulator that used Kafka and MQTT to publish synthetic sensor events and verified end-to-end message reliability, ensuring that the messaging infrastructure delivered all events without loss.

• Built an MQTT-to-Kafka bridge service that subscribed to specific MQTT topics and forwarded incoming messages to matching Kafka topics, allowing the platform to unify data ingestion and maintain consistent routing across microservices.

• Created a real-time sound-classification microservice in FastAPI that processed audio segments with ML models and published alerts to a dedicated Kafka topic whenever suspicious environmental noise was detected.

• Implemented an Apache Flink streaming service that consumed events from Kafka in real time and automatically triggered API calls to the sound-classification service for every incoming audio message, enabling low-latency, event-driven processing.

• Orchestrated the entire system data flow end-to-end by adapting an external team’s simulator to upload sound files into MinIO together with aligned metadata, ensuring the data seamlessly propagated through Kafka, Flink, and the ML inference services. This work guaranteed that the full pipeline—from simulated data to classification output—functioned reliably as an integrated system.