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Jorge Torrero

@jorgetorrero

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I build and validate reliable AI systems for healthcare, safety, and production operations.

United States

Message

What I'm looking for

I'm looking to build and validate reliable AI and GenAI systems, especially in healthcare or other high-impact environments where Responsible AI, model monitoring, explainability, and practical model-risk decisions matter.

I've built and validated AI, NLP, and generative AI systems for Mondo clients including UnitedHealth Group, Aramark, and Abbott, supporting healthcare payment integrity and operational decision workflows.

At Eli Lilly, I developed AI/NLP capabilities for a cloud pharmacovigilance platform processing approximately 15,000 documents daily. I benchmarked models across 1.2 million historical reports, expanded workflow audit coverage from approximately 70% to 100%, and helped reduce mobile case-notification response time from approximately 30 minutes to 5 minutes.

At Google, I engineered machine-learning-assisted release-readiness and monitoring capabilities for 400–600 monthly users. My work reduced CI/CD test duration from approximately 38 to 17 minutes, p95 API latency from approximately 650 to 240 milliseconds, and production detection time from approximately 20 to 4 minutes.

I focus on model validation, monitoring, explainability, Responsible AI, and translating model-risk findings into practical actions for engineering, risk, and business teams.

Experience

Work history, roles, and key accomplishments

Senior AI Engineer

Mondo

Jun 2024 - Jul 2026 (2 years 1 month)

Designed and validated healthcare AI and GenAI systems supporting payment integrity and operational decision workflows, applying model benchmarking, drift analysis, Responsible AI controls, model monitoring, and production-grade cloud deployment practices. Conducted independent Model Validation for healthcare AI models and implemented model monitoring with MLflow and AWS Model Monitor.

Python Hugging Face Lang Chain OpenAI PyTorch scikit learn Pandas Apache Spark MLFlow AWS Sagemaker SageMaker Model Monitor SHAP LIME Vector Databases Databricks

AI/ML Engineer II

Eli Lilly

Mar 2019 - Apr 2024 (5 years 1 month)

Developed and evaluated AI/NLP capabilities for a cloud pharmacovigilance platform processing approximately 15,000 incoming documents daily, combining model validation, data quality checks, monitoring, explainability, and secure workflow integration. Benchmarked model performance across approximately 1.2 million historical reports and 3 TB of data.

Python PyTorch TensorFlow scikit learn Pandas NLTK NLP REST APIs Microservices Apache Spark MLFlow PostgreSQL Docker Kubernetes OAuth

ML Software Engineer

Google

Nov 2016 - Jan 2019 (2 years 2 months)

Engineered machine-learning-assisted release-readiness and monitoring capabilities for an internal platform serving approximately 400–600 monthly users across 8–10 engineering and operations teams. Automated presubmit model and rule validation within CI/CD workflows and optimized REST APIs and data-processing paths.