This engagement centres on building and maintaining complete AI workloads—from the Python-based model APIs right through to the user-facing web layer—and taking them all the way to a hardened, enterprise-grade deployment on AWS and Azure. I need engineers who can fluidly move between backend AI development, frontend AI interaction design, and the operational disciplines required to release, monitor, and scale these solutions inside large organisations. On the backend you will write modular Python services that expose our models behind clean REST or GraphQL endpoints, handle data pipelines, and integrate seamlessly with AWS and Azure native services. On the frontend you will craft lightweight, responsive components that surface predictions, manage user feedback loops, and support real-time updates. Your enterprise deployment experience is just as critical: containerising the stack, automating CI/CD, applying security best practices, and tuning for high availability across both cloud providers. Expect close collaboration with my internal data science and DevOps teams; clear Git-based workflows; and a rapid, iterative release cadence. Acceptance criteria for each milestone • A tested Python backend that returns model outputs within agreed latency thresholds • A browser-based UI that consumes those endpoints and renders results responsively • Automated deployment artefacts (IaC, Docker, CI/CD) running in both an AWS and an Azure environment with monitoring hooks enabled If you thrive on end-to-end ownership across backend, frontend, and cloud, I’m ready to get started right away.