Gert-Jan Braeckevelt
Verified Expert in Engineering
Cloud Developer
Ghent, Belgium
Toptal member since December 20, 2021
Gert-Jan is a motivated machine learning and data engineer from Belgium. He is best described as a general problem solver that is not scared to go outside of a role description to get the job done. Gert-Jan has experience with advanced machine learning techniques, such as deep learning and general data engineering technologies like Spark.
Portfolio
Experience
- Python 3 - 7 years
- Docker - 5 years
- Kubernetes - 4 years
- Cloud - 4 years
- Forecasting - 3 years
- FastAPI - 2 years
- Spark - 2 years
- Apache Airflow - 2 years
Availability
Preferred Environment
MacOS, Visual Studio Code (VS Code), Amazon Web Services (AWS)
The most amazing...
...thing I did is automating and retraining data validation and data extraction for a forecasting project. I really enjoy seeing ML models fully in production.
Work Experience
Machine Learning Engineer
DPGMedia
- Set up multiple spark pipelines for support in a larger data framework.
- Contributed to developing a larger framework for ranking items for a sizeable consumer-facing product.
- Worked in an agile team using Jira and Confluence.
Data Scientist
Proximus
- Created a semi-autonomous framework for forecasting product revenues.
- Set up the guidelines for developing a computer vision-based quality assessment tool for fiber installations.
- Developed the models and supporting framework for optimizing the workforce planning of shops based on the prediction of footfall.
Machine Learning Engineer
Robovision
- Developed computer vision models for real-time assessment of the harvest quality in combined harvesters.
- Created an algorithm for optimizing the trajectory of gas pipelines.
- Developed computer vision models for assessing the quality of loading paper rolls on trucks.
- Made computer vision models for assisting the production of semiconductor products.
Experience
Adapting Facebook Prophet for Using Custom Trends
https://www.gertjanbr.com/blog/prophet/The model used is additive consisting of seasonal components, trends, and rare special events.
Exploring the TensorFlow 2 API with Gravitational N-body Problem
https://github.com/GertJanBraeckevelt/TF2_Gravitational_SimulationsTo explore the TensorFlow 2 API, I coded up the n-body problem. The project uses TensorFlow's built-in auto differentiation to calculate the forces between the different particles.
Education
Master's Degree in Physics
University of Ghent - Ghent, Belgium
Certifications
Building Modern Node.js Applications on AWS
Coursera
AWS Cloud Technical Essentials
Coursera
Skills
Libraries/APIs
TensorFlow, OpenCV, PyTorch, Node.js
Tools
Apache Airflow, AWS CodeBuild
Languages
Python 3, Gherkin, Python
Frameworks
Flask, Spark
Platforms
Docker, Kubernetes, Amazon Web Services (AWS), Azure, Kubeflow, MacOS, Visual Studio Code (VS Code)
Other
Forecasting, FastAPI, Physics, Cloud
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