I have a fully curated dataset and need an AI engineer who can turn it into a production-ready model that detects and classifies people, vehicles, and animals. The plan is to build a custom detector using YOLO and optimise it for low-latency inference with TensorFlow RT/TensorRT so it can run reliably on edge hardware as well as GPUs in the cloud. Here is what I’m expecting: • End-to-end training pipeline: data augmentation, transfer learning on the latest YOLO variant, and fine-tuning until we hit solid precision/recall numbers. • Exported weights plus a clean inference script (Python) that loads in under a second and returns bounding boxes, class labels, and confidences. • Clear documentation of your environment and commands so I can reproduce the results or retrain when new samples arrive. • A short performance report benchmarking FPS and mAP on my provided test split. You’ll have direct access to the dataset the moment we start, and I’m available for quick feedback loops on any class imbalance or annotation adjustments you spot. If you have recent examples of YOLO or TensorFlow RT work—especially anything with mixed object categories—share the repo link or demo video so I can see your approach. Let’s get this model trained and deployed.