Technical Stack & MLOps Workflow

I leverage a multidisciplinary stack to bridge the gap between high-level project coordination and technical implementation in Earth Sciences and emerging MLOps workflows.

AI & MLOps Exploration (Learning Phase)

• MLOps Foundations: Exploring end-to-end pipelines for AI surrogates using PyTorch, with a current focus on model deployment and monitoring workflows on the MN5 AI partition.
• Data Engineering for AI: Building robust workflows for manipulating large-scale datasets using Xarray, Dask, and NetCDF to support AI training and evaluation.
• AI Integration Studies: Investigating the strategic alignment of AI services with traditional Earth System Models (EC-Earth, NEMO) to enhance physical simulations.

HPC Orchestration & Project Management

• Workflow Coordination: Lead developer of Autosubmit templates, managing reproducible, multi-institution research workflows on Tier-0 systems.
• System Administration & Optimization: Expert in Slurm job scheduling, software environment management (LMOD/Conda), and optimizing hardware utilization for GPU-heavy AI workloads.
• Agile Coordination: Driving technical excellence within international consortia using Agile and Scrum methodologies for software development lifecycles.

Mathematical Foundations & Performance

• Numerical Excellence: Strong foundations in Applied Mathematics, including finite difference methods and multiscale averaging (♾️ ∫).
• Performance Programming: Developing high-performance model components in Fortran, C++, and Python.

Focused on the technical coordination and engineering infrastructure required to realize the next generation of AI-enhanced Climate Science.