Open to opportunities

Aimar Aguado

@aimaraguado

Machine Learning Engineer building production LLM and computer-vision systems with CERN-grade research rigor and measurable impact.

Spain

Message

What I'm looking for

I’m looking to build reliable production AI—LLMs, computer vision, and anomaly detection—using rigorous quantitative methods, robust deployment (AWS/CUDA/Jetson), and clear client communication. I want measurable impact through experimentation.

I’m a Machine Learning Engineer at Multiverse Computing, where I deliver production LLM systems grounded in research experience from CERN, ETH Zürich, and TU Munich. I built and deployed LLM-based emotion detection models, owning the full pipeline from data preprocessing to evaluation and model deployment, reaching 80% accuracy.

I focus on reliability and measurable improvement in real-world settings. I designed prompt engineering strategies that increased LLM performance by 10% using mathematical evaluation metrics (Scikit-learn, including F1-score), and I developed a computer vision pipeline (image alignment, YOLO-based detection, stereo distance estimation) optimized by 75% inference on Jetson hardware while communicating results to clients.

My research background strengthened how I approach uncertainty, optimization, and evaluation. In my Master Thesis at CERN & ETH Zürich, I built unsupervised anomaly detection models for fraud detection using Python, PyTorch, deep learning, and Graph Neural Networks—achieving a 5% performance gain and improving mathematical analysis by 25% through statistical analysis, hypothesis testing, and hyperparameter tuning.

Experience

Work history, roles, and key accomplishments

Current

Machine Learning Engineer

Current

Multiverse Computing

Jan 2026 - Present (5 months)

Built and deployed LLM-based emotion detection models achieving 80% accuracy, owning the full pipeline from data preprocessing to evaluation and deployment. Designed prompt engineering strategies to improve LLM reliability (10% performance gain) and developed a computer vision pipeline with 75% inference optimization on Jetson hardware.

LLM Prompt Engineering Computer Vision YOLO Stereo Distance Estimation CUDA AWS

Master Thesis Researcher

CERN & ETH Zürich

Sep 2024 - Sep 2025 (1 year)

Developed unsupervised anomaly detection models for large-scale physics datasets, improving fraud-detection performance by 5% using deep learning and graph neural networks. Performed statistical analysis, hypothesis testing, and hyperparameter tuning, improving mathematical analysis by 25% and presenting findings to CERN/ETH committees.

Anomaly Detection PyTorch Statistical Analysis Hypothesis Testing Hyperparameter Tuning Mathematical Optimization Python Data Analysis

Research Intern

CIC Nanogune

Jun 2022 - Aug 2022 (2 months)

Ran simulations on low-energy electron radiation effects on graphene and water, producing results that led to a Bachelor’s thesis offer. Strengthened scientific communication and teamwork through research collaboration and presentation.

Data Simulation Low Energy Electron Radiation Graphene Scientific Communications Teamwork Data Analysis Python

Education

Degrees, certifications, and relevant coursework

ETH Zürich

Master's Thesis, Machine & Deep Learning for anomaly detection in high-energy physics

2024 - 2025

Grade: 6.0/6.0

Activities and societies: Project mobility; thesis research at CERN (Geneva).

Completed a Master's thesis project at ETH Zürich in collaboration with CERN (Geneva) on machine and deep learning for anomaly detection in high-energy physics.

Technical University of Munich

Master of Science in Applied and Engineering Physics, Applied and Engineering Physics (High Energy Physics, Data Analysis and AI)

2023 - 2025

Grade: 1.1

Activities and societies: Specialization in High Energy Physics, Data Analysis and AI; top 3rd percentile.

Earned a Master of Science in Applied and Engineering Physics with a specialization in High Energy Physics, Data Analysis, and AI. Final grade: 1.1 (top 3rd percentile).