I have a growing pipeline of AI-driven products that need to move from idea to production over the coming year. Every project centres on developing brand-new applications, so I’m looking for engineers who can own the full lifecycle: turning raw data into a reliable, cloud-hosted model and keeping it healthy in the wild. The work spans three core domains—Natural Language Processing, Computer Vision, and Predictive Analytics—and typically involves: • Data preprocessing and cleaning • Model development and training (Python, TensorFlow or PyTorch preferred) • Containerised deployment and continuous monitoring (Docker/Kubernetes, AWS or GCP) You’ll step into projects such as conversational agents, visual quality-inspection tools, and forecasting engines, collaborating with me on architecture choices, experiment design, and MLOps best practices. Clean, well-documented code and reproducible experiments are non-negotiable; I want to be able to pick up any model six months later and understand exactly how it was built. Deliverables for each assignment will generally include: 1. A version-controlled repo with preprocessing pipelines, training scripts, and inference code 2. A reproducible environment file or Docker image 3. Deployed endpoint or service with basic monitoring hooks 4. A short hand-off report outlining results, limitations, and next steps If you’re interested in a long-term collaboration where you can tackle diverse, green-field challenges, tell me about one recent NLP, CV, or forecasting project you took from dataset to deployment and the specific problems you solved along the way.