The integration of machine learning (ML) components into Earth System Models (ESMs) holds significant potential for improving climate simulations, but it is often constrained by challenges related to interoperability, reproducibility, and computational efficiency. This paper presents a novel coupler-based approach that combines the OASIS3-MCT coupling library with Eophis, a high-performance Python library, to seamlessly deploy ML components in ESMs. By leveraging OASIS3-MCT's robust communication infrastructure, Eophis abstracts the technical complexities of coupling, enabling users to integrate Python-based ML models or analytical parameterizations into geophysical simulations with minimal overhead. Eophis provides a modular interface for defining data exchanges, synchronizing time steps, and managing parallel communications, including advanced features such as halo construction for Convolutional Neural Networks and hybrid CPU/GPU execution. We evaluate this framework by coupling the NEMO4 ocean model with both ML-based and analytical parameterizations, demonstrating scalability, asynchronous execution, and flexible resource allocation. Benchmarking results show that this approach allows to deploy Python components in NEMO without affecting time-to-solution while offering a user-friendly, reproducible, and collaborative workflow. By lowering the barrier to hybrid modeling, this work facilitates the integration of ML components into ESM workflows. The combination of OASIS3-MCT and Eophis offers a practical solution for bridging the gap between ML and climate modeling, supporting innovation in Earth system science while maintaining reproducibility and ease of use.