Backend Engineer, Video Analytics and Predictive Maintenance

About the role

You will build the pipeline that turns camera streams into typed events and tool sensor streams into early alerts that name the affected sensors. The first release is classical computer vision and time series. The language model layer comes after that works.

Key responsibilities

• Build edge detection and tracking: RTSP from the DVRs into GStreamer or DeepStream, inference through ONNX Runtime or TensorRT, and a frame budget per camera that holds when all thirty cameras are busy
• Create typed events: one event per track with a stable identifier, a confidence, a one-sentence caption and a pointer to the clip, published over MQTT with a local spool that drains after a network cut
• Develop central ingest: PostgreSQL with TimescaleDB to deduplicate, place the event on the site plan through the camera calibration, correlate into incidents, aggregate so every dashboard panel answers under a second
• Implement Equipment Watch: multivariate sensor streams through RFOD and CAD, so an alert carries the affected sensors and a feature attribution rather than a bare score, measured against labelled failure windows
• Build Replay: role check, audit record, signed ticket, WebRTC relay from the edge box to the browser, held in memory only
• Argue with the specification where it is wrong

About you

• One to three years of Python in production
• Video work in one of OpenCV, GStreamer or DeepStream
• Model inference through ONNX Runtime, TensorRT or PyTorch
• SQL beyond the basics
• Docker and Linux
• Jetson deployment, MQTT, TimescaleDB, time series anomaly detection and time spent in a fab or plant all help