AI Engineer | Biomedical AI Researcher | Builder

Building reliable AI systems in healthcare, from pharmaceutical operations to biomedical research.

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

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

At Laboratorios Bagó del Perú, I lead AI initiatives for pharmaceutical commercial and medical workflows. My work spans agentic systems, RAG, evaluation, analytics, deployment, and cross-functional delivery. These systems serve 120+ users, with a clinical RAG platform handling 7,000+ queries per week.

My biomedical AI research focuses on generative modeling and anomaly detection for fetal MRI. I contributed to work published in NeuroImage and presented at ISMRM, OHBM, and MIT-MGB AI Cures.

• Taypi Observatory: local-first workbench for discovering, executing, auditing, and replaying MCP and A2A interactions
• MRIxFields 2026: cross-field MRI translation and harmonization for the MICCAI challenge
• Production agentic and RAG systems for pharmaceutical workflows
• Graduate study, research collaboration, and innovation leadership opportunities

A local-first visual workbench for MCP and A2A interactions, with approval gates, append-only traces, replay, protocol inspection, reliability reports, and deterministic failure injection.

Deep generative normative modeling for structural and developmental anomaly detection in fetal brain MRI.

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

Synthetic identity infrastructure for facial consistency using embeddings, geometric landmarks, and longitudinal drift tracking.

• Conditional deep generative normative modeling for structural and developmental anomaly detection in the fetal brain
  NeuroImage, 2025
  https://doi.org/10.1016/j.neuroimage.2025.121442

• 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

Languages: Python SQL Bash
Frameworks: PyTorch FastAPI Flask Next.js scikit-learn
Infrastructure: Docker REST APIs SSE CI/CD Oracle SQL
AI: LLMs RAG Agentic Systems Evaluation Generative Models Medical Imaging

• Built AI systems used by 120+ internal users
• Scaled a clinical RAG platform to 7,000+ queries per week
• Led technical delivery across engineers, business teams, and external specialists
• Co-authored peer-reviewed research in NeuroImage
• Organized a two-day AI hackathon with 70+ participants and 17 evaluated projects

• Fully funded graduate study
• Research collaborations
• Applied AI and innovation leadership roles
• Health AI, biotechnology, and agentic-systems opportunities
• Focused technical consulting engagements

• Website: https://simonamador.com
• Email: samador0208@gmail.com
• 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.