Philadelphia, Pennsylvania, United States
I help engineering teams build real AI systems — not just understand them in theory. For the past 2+ years, I have delivered corporate AI/ML training programs through Educate360 for large-scale U.S. engineering organizations. My sessions consistently receive 5-star reviews from engineering teams across industries. My courses are practical, notebook-based, and designed for teams that need to understand how modern AI systems actually work — not just hear about them in slides. I teach and build around the full AI learning path: • Python for Data Science • Python for Machine Learning • Deep Learning • OpenAI API • RAG pipelines • Agentic AI systems • Production-minded AI workflows My teaching style combines systems thinking, real code, and engineering judgment. I focus on helping teams understand the "why" behind the architecture: how data becomes features, how models become decisions, how retrieval becomes memory, and how agents become useful software systems. Academically, I hold a PhD in Systems Engineering from Universidad Nacional Federico Villarreal, an MS in Software Engineering from Universidad Ricardo Palma, and a BS in Electrical Engineering from Drexel University. I also teach graduate-level Machine Learning and Deep Learning at UNMSM and UAP. My delivery model is 100% remote via Zoom, available globally. I am especially useful for teams that want practical AI adoption without hype: clear explanations, working notebooks, production mindset, and a path from fundamentals to real AI systems. For corporate training, AI/ML workshops, or OpenAI API · RAG · Agentic AI programs — send me a LinkedIn message.
Delivering high-impact virtual training to professionals across industries through Educate360’s corporate learning programs (New Horizons and partner organizations). Key highlights include - Teaching four main courses: - Introduction to Python – Basic types, control flow, logic, functions - Python for Data Science – Numpy, Pandas, Matplotlib, Seaborn - Python for Machine Learning – Regression, classification, scikit-learn best practices - OpenAI API Bootcamp – Chat Completions, Prompt Engineering, Image Generation, Language Translation, Whisper API, Embeddings, Fine-Tuning, and OpenAI Agents (Responses API + Vector Store + Web Search for RAG) - Data Literacy – Understanding the DIKW pyramid, reading dashboards, identifying insights, avoiding misleading stats, and making evidence-based decisions using real-world datasets (e.g., Spotify Music) Fully designed and modernized the OpenAI API Bootcamp curriculum, now taught to advanced teams including Amazon’s FTR department Completed over 39 sessions between 2023 and 2025.
Teaching Neural Networks and Deep Learning in the Master's Program in Data Science — a 10-week graduate-level course built entirely from scratch, covering both sequential and convolutional architectures. Delivered remotely via Zoom to a cohort of 25 graduate students who are working professionals in data and technology roles. Curriculum — First half (NLP and Sequential Models): - Neural network fundamentals: architecture, activation functions, backpropagation, gradient descent - Building models with TensorFlow and Keras - Regularization: Dropout, Batch Normalization, Early Stopping - Recurrent Neural Networks (RNN) and vanishing gradient problem - Long Short-Term Memory networks (LSTM) - Gated Recurrent Units (GRU) - Bidirectional LSTM and GRU for sequence modeling - Applied NLP: sentiment analysis on real datasets (IMDB) - Text preprocessing, tokenization, embeddings, TextVectorization Curriculum — Second half (Computer Vision): - Convolutional Neural Networks (CNN): filters, pooling, feature maps, architecture design - Transfer Learning with pre-trained models - Object Detection with YOLO - Dataset annotation and management with Roboflow - End-to-end computer vision pipelines All course materials, notebooks, and assessments developed independently by the instructor from zero.
Teaching Machine Learning in the Master's Program in Artificial Intelligence — an intensive 4-week curriculum designed from scratch to bring working professionals to production-level ML proficiency. Courses are delivered entirely remotely via Zoom. Average cohort size: 50 graduate students per cycle — exceptionally large for a private Peruvian institution. Curriculum covers the full supervised learning pipeline: - Exploratory Data Analysis with Pandas, NumPy, and Seaborn - Feature Engineering and categorical encoding (One-Hot, Label, Ordinal, Target Encoding) - Train/test split discipline and data leakage prevention - Regression: Linear, Ridge, Lasso, Polynomial, ElasticNet - Classification: Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, SVM, KNN - Class imbalance handling: SMOTE, class_weight, threshold tuning - Model evaluation: Accuracy, Precision, Recall, F1, ROC-AUC, Confusion Matrix, Classification Report - Cross-validation and hyperparameter tuning with GridSearchCV - Pipeline construction with Scikit-Learn - Deployment mindset: from notebook to production-ready code All instruction uses Python exclusively — no managed consoles, no black-box platforms. Students build and understand the mechanics of every model they train. 6 cohorts delivered (2025–2026). 300+ graduate students trained.
Providing part-time advisory support on AI integration strategies for Diti Technology, a Lima-based digital solutions company. Key contributions include: - Evaluating the use of OpenAI API tools (e.g., GPT-4o, embeddings, Whisper) for product innovation - Offering guidance on LLM integration for client-facing automation and chat solutions - Sharing insights on best practices for deploying scalable GenAI-powered features in business contexts - Supporting the team’s understanding of AI capabilities, ethical considerations, and roadmap planning
Teaching in the Master’s Program in Systems and Informatics Engineering with a focus in Software Engineering (graduate-level) at the oldest and most prestigious university in the Americas. Key responsibilities include: - Developed and currently teach the graduate course “Machine Learning and Big Data” as part of the official Master’s curriculum at UNMSM, including full syllabus design, learning objectives, and project-based assessments. - Designing and delivering the course “Machine Learning and Big Data” for software engineering graduate students - Integrating hands-on projects using Python, scikit-learn, TensorFlow, PySpark, and the OpenAI API - Emphasizing software design patterns, MLOps principles, ethical AI, and thesis-aligned applications - Guiding students through building production-level ML systems with Docker, FastAPI, and CI/CD pipelines - Fostering critical thinking, model interpretability, and responsible AI development in real-world contexts