AI Engineer | Researcher | Builder

Engineering trustworthy AI for clinical and enterprise applications.

Website • LinkedIn • Google Scholar • GitHub • X/Twitter

I build AI systems at the intersection of healthcare, research, and product engineering.

Currently, I lead the development of enterprise LLM agent systems at Bagó, where I work with a team of 3 engineers to design AI solutions used across pharmaceutical workflows. My research focuses on generative models and anomaly detection for fetal MRI, with work published in NeuroImage and presented at ISMRM, OHBM, and MIT-MGB AI Cures.

I am interested in graduate study (MSc/PhD), research collaborations, and startup opportunities in health AI, biotechnology, and applied machine learning.

• Enterprise LLM agent systems for pharmaceutical workflows
• Medical imaging AI (MRI, OCT, CT)
• Generative models and anomaly detection
• Clinical decision support systems
• Trustworthy and interpretable AI

A sanitized architecture case study of an enterprise multi-agent system built with Python, Flask, Oracle SQL, RAG, and commercial LLM APIs.

Deep generative normative modeling for fetal brain anomaly detection using MRI.

Production-grade synthetic identity infrastructure that enforces facial consistency using embeddings, geometric landmarks, and longitudinal drift tracking.

• Conditional Deep Generative Normative Modeling for Fetal Brain Anomaly Detection
  NeuroImage, 2025
  https://doi.org/10.1016/j.neuroimage.2025.121089

• Conditional Deep Generative Normative Modeling for Structural and Developmental Anomaly Detection in the Fetal Brain
  ISMRM, 2025

• Deep Generative Anomaly Detection for Structural Anomalies in Fetal Brain with Ventriculomegaly
  OHBM, 2024

• Covariate-Conditioned Fetal MRI Anomaly Detection
  MIT-MGB AI Cures, 2024

Python SQL Bash

PyTorch Flask FastAPI scikit-learn

Docker REST APIs CI/CD Oracle SQL

LLMs RAG Generative Models Anomaly Detection

• Medical Imaging
• Foundation Models
• Generative Modeling
• Clinical AI
• Trustworthy AI
• Digital Health

• Led a team of 3 engineers and interns
• Built enterprise AI systems used by more than 120 internal users
• Published peer-reviewed research in a leading neuroimaging journal
• Bridging academic research and production engineering

• Enterprise LLM agent systems for pharmaceutical workflows
• Open-source tooling for AI engineering
• Research toward clinically impactful medical AI

• MSc and PhD opportunities
• Research collaborations
• Health AI startup roles
• Applied machine learning leadership positions

Outside of engineering and research, I enjoy:

• Running
• Strength training
• Scientific writing
• Technology strategy
• Creating educational content about AI

• Website: https://simonamador.com
• Email: [email protected]
• LinkedIn: https://www.linkedin.com/in/simon-amador/
• Google Scholar: https://scholar.google.com/citations?user=uiDl364AAAAJ

Building AI systems that translate research into real-world impact.