The project centers on building a production-ready TensorFlow 2.x model that classifies tabular data delivered to us through an internal API. I have the API specifications and sample payloads ready; you will turn those streams into a clean training pipeline, engineer the right features, and iterate until the classifier meets our performance targets in real-world tests. Scope of work • Data pipeline – pull the API data, handle preprocessing, and produce TensorFlow-friendly datasets for train/val/test splits. • Model development – design, train, and tune a deep learning architecture suitable for tabular inputs (e.g., wide & deep, Transformer, or other proven structures). • Optimization – experiment with hyperparameters, regularization, and callbacks to achieve stable accuracy and minimal inference latency. • Packaging – export the finished model as a SavedModel plus concise Python inference wrapper, ready to drop into our existing service. • Integration – provide a small integration script/snippet that consumes live API data, calls the model, and returns the predicted class in the required JSON format. Acceptance criteria 1. End-to-end notebook or script demonstrates ≥ X% validation accuracy (we will agree on the exact threshold upfront). 2. Inference time per record ≤ Y ms on our target hardware. 3. Codebase follows PEP 8, is reproducible with requirements.txt, and runs under Python 3.10. 4. Delivered artefacts: source code, trained model files, brief README covering setup, training, and deployment steps. If you routinely work with TensorFlow tabular pipelines, understand data APIs, and can hand off clean, production-grade code, this assignment should feel straightforward. I will supply API keys, schema docs, and label definitions as soon as we start.