I have several software initiatives that require robust, production-ready machine-learning models. I’m looking for an AI engineer who can take a dataset from raw form through model design, training, validation, and deployment, then document the entire pipeline so it can be maintained by my in-house team. You should be comfortable choosing the most appropriate algorithms, handling feature engineering, tuning hyper-parameters, and explaining trade-offs in accuracy, speed, and resource use. Typical tools in my stack include Python, TensorFlow, PyTorch, scikit-learn, and Docker, so experience with these will be valuable. Deliverables • Clean, well-commented code in a Git repo • Reproducible training scripts and environment files • A trained model (or models) saved in a portable format • Evaluation report with key metrics and error analysis • Deployment-ready API or packaged module • Brief hand-off guide so my developers can extend the work Acceptance criteria • Model meets or exceeds agreed performance benchmarks on a held-out test set • Training pipeline runs end-to-end in the provided environment without manual intervention • Code passes automated linting/tests and follows PEP 8 standards • Documentation is clear enough for a new engineer to replicate results in one attempt If this aligns with your expertise in machine learning, let’s discuss data specifics and milestones to get started right away.