PhD in Mathematical Finance & Stochastic Calculus, MS in Software Engineering—architecting ML platforms and Generative AI solutions at enterprise scale for banking and tech.
I'm a Co-Founder, Technical Lead, and Full-Stack AI/ML Engineer with a unique blend of deep mathematical expertise and hands-on engineering skills. My PhD in Mathematical Finance & Stochastic Calculus, combined with a Master's in Software Engineering, enables me to bridge the gap between theoretical innovation and practical implementation.
I co-founded Asclepius, a clinical AI SaaS platform for doctors in LATAM — designing and shipping the full-stack product end-to-end, from Django/React frontend to AWS CloudFormation infrastructure, LangGraph AI pipelines, and Stripe billing. At Banamex, I lead the development of an enterprise ML platform similar to Uber's Michelangelo, integrating Generative AI solutions that process 1,000+ daily customer interactions with 95% accuracy, reducing time-to-production by 60% and operational costs by 30%.
My research in quantitative finance and mathematical modeling has been published in top peer-reviewed journals, and I continue to apply advanced stochastic calculus and machine learning techniques to solve complex real-world problems.
Python, R, JavaScript, TypeScript, SQL
TensorFlow, PyTorch, Scikit-learn, MLflow
OpenAI GPT-4, Whisper, Google Gemini
PySpark, Hadoop, Cloudera
FastAPI, Flask, PostgreSQL
AWS, Heroku, Airflow, Docker
Universidad Nacional Autónoma de México
Focus: Mathematical Finance, Machine Learning, Probability & Statistics
Advisor: Prof. Pablo Padilla Longoria
UNAM-BBVA Foundation AwardUniversidad de los Andes
Focus: Algorithm Design, Software Architecture, Full-Stack Development, DevOps
University of Texas at Austin & GreatLearning
Universidad Nacional Autónoma de México
Focus: Probability Theory, Statistics, Mathematical Finance
Advisor: Prof. Ramsés H. Mena
Pontificia Universidad Javeriana
Minor: Economics | Focus: Mathematical Analysis, Econometrics
Advisor: Prof. Gerardo Román Chacón
Asclepius
Mexico City, Mexico
Co-founded and led the technical development of a clinical AI platform for doctors in LATAM. Designed the full system architecture and built four core AI products: a Clinical Note Copilot for automated SOAP note generation with ICD-11 codes, a Guideline-to-Action Engine converting medical guidelines into patient-contextualized checklists, Asclepius Axon for voice-powered patient intake using LangGraph SSE pipelines, and an AI Clinical Assistant backed by pgvector semantic search.
Designed and deployed full AWS infrastructure using CloudFormation infrastructure-as-code, including VPC networking, EC2 Auto Scaling Group behind an Application Load Balancer, RDS PostgreSQL with pgvector, and Amazon MQ (RabbitMQ) for Celery task queue. Implemented zero-credential CI/CD via GitHub Actions with OIDC authentication and automated blue-green deployments.
Defined and implemented the subscription model with Basic and Premium plans, integrating Stripe Checkout and webhooks. Built Google OAuth authentication with HttpOnly cookie-based JWT sessions. Oversaw the entire product lifecycle from architecture and development to production deployment.
Independent Contractor
Remote
Developed an MVP for a Praktika-like language learning application featuring real-time conversational AI for grammar practice. Built microservices architecture using Docker containerization, FastAPI for API endpoints, and VLLM as LLM server provider with advanced prompt engineering and guardrails implementation. Created robust backend infrastructure with Django and PostgreSQL for user management, course creation, and chat history retrieval. Designed responsive frontend using React, Vite, and Tailwind CSS for seamless user experience.
Architected and deployed a production clinical AI SaaS platform for doctors in LATAM (asclepius-ai.com). Built a Clinical Note Copilot that generates SOAP notes with ICD-11 codes and prescription drafts from encounter data, a Guideline-to-Action Engine that converts clinical guidelines into patient-contextualized checklists with source citations, and Asclepius Axon — a voice-powered patient intake module using LangGraph SSE pipelines for real-time structured extraction. Implemented an AI Clinical Assistant for free-text clinical Q&A backed by pgvector semantic search. Backend built with Django 5.x, DRF, and Celery with RabbitMQ; deployed on AWS using CloudFormation infrastructure-as-code (EC2 ASG, ALB, RDS PostgreSQL with pgvector, Amazon MQ). Integrated Stripe subscription billing and CI/CD via GitHub Actions with OIDC authentication.
Banamex
Mexico City, Mexico
Leading the design and development of an enterprise ML platform similar to Uber's Michelangelo, integrating FastAPI, MLflow, Spark, TensorFlow, and Scikit-learn to accelerate model development and deployment processes across the bank.
Developed ETL pipelines integrated with ML/NN models using PySpark, TensorFlow, and Scikit-Learn for advanced feature engineering, improving credit model KPIs by up to 5%.
Designed and deployed the first enterprise recommender system using ALS in PySpark, integrated with Adobe Ecosystem and AWS services, solving cross-border data transfer challenges while serving personalized content to mobile applications.
Designed and implemented Airflow orchestration on Cloudera for automated capacity monitoring, triggering alerts when databases reach maximum capacity on Hadoop clusters and RHEL servers.
Built resilient FastAPI applications for ETL pipeline orchestration, enabling efficient resource management and automated scaling.
Developed an ML-powered ATM location optimization application using FastAPI and PyArrow for efficient data lake access, featuring interactive Google Maps-like visualization and WhisperAI voice search capabilities.
Universidad Nacional Autónoma de México
Mexico City, Mexico
Conducted cutting-edge research in quantitative finance, applying machine learning techniques and stochastic calculus to develop novel algorithmic trading models, resulting in peer-reviewed publications in top-tier financial journals.
Co-designed and implemented sophisticated pricing algorithms for complex financial derivatives in American markets using Python and R, achieving improvement in pricing accuracy compared to traditional Black-Scholes models.
Applied advanced stochastic differential equations and Monte Carlo simulations to model market volatility and risk assessment, contributing to award-winning doctoral thesis recognized by UNAM-BBVA Foundation.
Authors: Ivan D. Peñaloza-Rojas, Pablo Padilla Longoria
Computational Economics, Springer, Netherlands, 2021
Developed novel pricing algorithms for financial derivatives in markets with incomplete information and constraints, combining stochastic calculus with machine learning techniques.
Type: PhD Dissertation, 2021
Universidad Nacional Autónoma de México
UNAM-BBVA Foundation AwardType: Master's Thesis, 2016
Universidad Nacional Autónoma de México
Type: Bachelor's Thesis, 2012
Pontificia Universidad Javeriana
I'm always interested in discussing new opportunities, collaborations, or innovative projects in ML/AI and data science.