Engineered automated, no-code training and evaluation pipelines
for large language models (LLMs), transitiong the team to fully
open-source frameworks from proprietary cloud dependencies.
This initiative has faciliated day-one training
and evaluation of newly released LLMs, significantly enhacing
operational efficiency.
Led the fine-tuning and deployment of a model for data
extraction, achieving over 98% reduction in
operational expenses from $60,000 to under
$1,000 monthly, while delivering superior
performance compared to existing proprietary closed-source
models.
Technologies: Large Language Models, Python
(PyTorch, HF Transformers), Docker, AWS.
Microsoft
Amsterdam, Netherlands
Cloud
Solutions Architect Intern
Developed a fraud-detection system that detected anomalies of up
to 10M USD in Azure consumption for clients across
Western Europe.
Integrated image segmentation model into a knowledge mining
system to extract, name, and index chemical structure images,
eliminating search time for un-indexable research documents for a
top-15 bioscience company in the Netherlands.
Designed and trained a robotic arm simulation using the MuJoCo
Engine and Microsoft Project Bonsai to handle soft bodies with
precision.
Integrated internal build system to automate repository creation
and user authorization, resulting in a one-click solution that will
save approximately 800 SWE hours annually and
enable mass adoption of our framework by 200+ internal
teams.
Created a script to automate the generation of config-files for
stress-testing, reducing deployment times of tests by approximately
75%.
Developed a customer chat-bot using Amazon Kendra & Lex to
query internal wikis, allowing instantaneous customer response time
for FAQs about our framework.
Technologies: Java (backend), Python, AWS,
Natural Language Processing
Projects
RL Laser-Tag Environment
Modelled a reinforcement learning (RL) environment in Unity to
mimic a game of laser-tag.
Utilised the ML-Agents framework and the stabe-baselines3 PPO
implementation to train the agents.
Reproducing
Reinforcement Learning Papers
Implemented fundamental reinforcement learning algorithms from
scratch like DQN, PPO, and SAC using PyTorch.
Matched the performance of standard libraries like CleanRL &
StableBaselines3 on the MuJoCo Benchmark.
Awards
Winner (3/2000+) of Andrew Ng’s Data-Centric AI
competition. Presented solution at the NeurIPS Datasets &
Benchmark track.
Academic Excellence Award by the Dutch Society of Sciences
(KHMW) for rank 1/350 at TU/e.
Education
BSc
Computer Science, Honors AI Eindhoven,
Netherlands
Eindhoven
University of Technology (TU/e)
Current GPA: 8.9/10 (US 4.0/4.0).
Thesis: Exploring the Landscape of Differentiable
Architecture Search with Reinforcement Learning under Dr. Joaquin
Vanschoren.
Teaching Assistant for the courses: Introduction To
Programming and Data Structures & Algorithms.