Django Backend Engineer for Optimizing AI Detection Models on Live Video Feeds

We run an AI detection system on live camera feeds, and we need a Django backend engineer to make the model-serving side faster, more stable, and easier to scale. The detection models already exist. The job is to make the backend that feeds them frames and handles their output run well in production. What you’ll work on: Profile and optimize the pipeline from live stream to model inference to results, and remove latency and throughput bottlenecks Build a frame-handling layer in Django that manages concurrent streams without dropping or backing up frames (threading, multiprocessing, or Celery workers) Implement frame-skipping, batching, queueing, and backpressure strategies so inference keeps up under load Build the backend around the detection models: loading, versioning, swapping, and rollback of model versions without downtime Store and process detection results in PostgreSQL, with careful indexing and query optimization for high-volume event data Build the Django REST Framework endpoints for detection events, stream and camera configuration, and model status Add monitoring and logging for latency, FPS, queue depth, and failure rates, plus automatic stream reconnect and recovery Document the architecture and optimizations you make Required skills: Strong Python and Django/DRF, with production experience Concurrency and performance tuning (threading, multiprocessing, asyncio, Celery) PostgreSQL query optimization Redis or a similar queue/cache Experience profiling and debugging slow or unstable backend systems Nice to have: Familiarity with OpenCV and RTSP streams Experience serving ML models (ONNX, PyTorch, Triton) Docker and CI/CD