David De La Iglesia Castro
Señor AI Engineer at Mozilla.ai
Pontevedra, Galicia, Spain
Summary
🤩
Rockstar🎓
Top SchoolDavid De La Iglesia Castro is a Señor AI Engineer with a decade of experience building production-ready ML and computer vision systems, currently at Mozilla.ai. He combines backend and DevOps expertise with deep computer vision know-how, contributing to high-profile open-source projects like MMDetection, MMSegmentation and DVC to improve experiment management, logging and model deployment. His work spans the full ML lifecycle—from data-versioning and experiment hooks (Mlflow, Dvclive, Wandb) to TorchServe-ready segmentation models and robust inference fixes on CPU. Prior roles at Iterative, Gradiant and KITRO show a pattern of moving research prototypes into reliable engineering artifacts. Based in Pontevedra, Spain, he brings a practical systems mindset (Octree/VoxelGrid and point-cloud processing in pyntcloud) that helps bridge research and scalable deployment. Colleagues rely on him for non-obvious strengths: improving developer workflows and automation that reduce friction across teams.
10 years of coding experience
9 years of employment as a software developer
Grado en Arquitectura Técnica, Ingeniería de edificación, Grado en Arquitectura Técnica, Ingeniería de edificación at Universidade da Coruña
Spanish, English, Galician
Stackoverflow
2,506reputation
402kreached
62answers
9questions
computer-vision
top-5%
python
top-5%
Github Skills (47)
continuous-deployment10
git10
algorithms10
pytorch10
scipy10
visualization10
pytest10
python10
testing10
semantic-segmentation10
pandas10
command-line-interface10
machine-learning10
mlflow10
data-structure10
Programming languages (13)
Github contributions (5)
iterative/dvc
Apr 2021 - Jan 2023
🦉 Data Versioning and ML Experiments
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:8 releases, 578 reviews, 131 commits in 1 year 10 months
Contributions summary:David primarily contributed to enhancing the functionality and maintainability of the DVC project, focusing on experiment management and data versioning features. They added support for wildcard patterns in experiment include/exclude parameters, incorporated auto-refresh options for HTML rendering, and fixed issues related to running in subdirectories and handling of remote authentication. Their work extended to improving command-line interfaces and overall codebase, highlighting both back-end development and system automation skills.
data-version-controlml-modelspythonversion-controldata-science
open-mmlab/mmcv
Apr 2020 - Dec 2021
OpenMMLab Computer Vision Foundation
Role in this project:Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:6 reviews, 9 commits, 12 PRs in 1 year 8 months
Contributions summary:David primarily contributed to the integration of the MLflow logging hook, enhancing the repository's experiment tracking capabilities. They implemented and refined the `MlflowLoggerHook`, addressing issues related to setup, metric logging, and model logging. Furthermore, they also refactored and added functionality to logging hooks, including DvcliveLoggerHook and WandbLoggerHook, enhancing the project's logging and experiment tracking capabilities. Additionally, the user made enhancements to existing features, such as adding functionality and fixes, to the build and test automation of the project.
visiondeep-learningcomputer-visionfoundationopenmmlab
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