I’m ready to push a new natural-language-processing product from concept to production and need a senior Python engineer who can own the entire ML lifecycle. You’ll turn raw text data into a robust, scalable service that delivers measurable business value. Here’s what I expect you to drive: • Model research and rapid prototyping in TensorFlow, PyTorch, and Scikit-Learn, selecting the best architecture through solid experimentation and metrics. • Data-pipeline design—cleaning, feature generation, versioning, and automated retraining—so every experiment is fully reproducible. • Production-grade deployment: containerised inference endpoints, CI/CD, monitoring, and autoscaling on the cloud platform of your choice. • Clear documentation and unit / integration tests that make ongoing maintenance straightforward for the wider engineering team. Acceptance criteria • An API or microservice that returns NLP predictions with latency under 100 ms and horizontal scalability demonstrated under load testing. • End-to-end reproducibility: one command spins up infrastructure, trains the model, and deploys it. • Coverage reports and README that allow another engineer to reproduce results in under an hour. If you’re comfortable leading cross-functional discussions, enjoy balancing research depth with shipping deadlines, and have a track record of launching NLP systems at scale, let’s talk.