AI and technology leader specializing in artificial intelligence, data science, computer vision, and automation solutions for manufacturing and operational environments. Proven experience leading technology initiatives from concept through deployment, enabling efficiency improvements, process optimization, and data-driven decision making.

Strong background in machine learning, MLOps, and end-to-end solution development, combined with experience coordinating cross-functional teams, aligning technical initiatives with business objectives, and supporting the successful adoption of emerging technologies.

Projects

Deep Reinforcement Learning for resource allocation in wireless networks

This project utilized Deep Reinforcement Learning (DRL) and Transfer Learning (TL) for power allocation in wireless cellular networks through simulation environments using Pytorch. This project resulted in three published refereed articles in top journals from Q1 and Q2 in the computer science area.

AI Repository Assistant

This project is an AI-powered assistant for GitHub repositories. It indexes documentation, enables context-aware searches, and provides real-time answers through a streaming chat interface, helping users quickly find relevant code and information.

Recognition of eye diseases with neural networks

This project involves the development of a convolutional neural network (CNN) for detecting ocular diseases using the ODIR-5K database from the Kaggle platform. The CNN was designed using Python with Keras. The model achieved an accuracy of 89.2% in detecting various ocular diseases such as diabetic retinopathy, cataracts, glaucoma, and age-related macular degeneration.

Explainable AI (XAI) for beer brand classification

This project combines a Convolutional Neural Network (CNN) based on the VGG16 architecture with GRAD-CAM, an explainable AI method, to interpret the model's focus for beer brand classification. The CNN is fine-tuned with additional layers, achieving an accuracy of 91.6%. Data preprocessing and augmentation were carried out using libraries such as Keras and Sklearn.

San Diego home price prediction

This project involved collecting data through web scrapping, cleaning and processing data, and training a regression model to predict the home price based on the apartment's neighborhood, square feet, and the number of beds and baths. Finally, the model was hosted on a web page.

Bellabeat capstone project with R

This capstone project is part of the Google Data Analytics Course. The project consists of answering a business task based on the data provided. The data cleaning and data visualization was performed in R.

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Career Overview

Alexis Anzaldo

SKILLS

• Programming: Python (TensorFlow, PyTorch, Keras, Scikit-Learn, OpenCV, Pandas, NumPy, Matplotlib, Seaborn), SQL.

• AI/ML Techniques & Models: CNN, YOLO, Transfer Learning, Reinforcement Learning, Explainable AI (GRAD-CAM), Data Augmentation, Outlier Detection, Regression, Large Language Models (GPT, Claude — fine-tuning, embeddings, prompt engineering)

• Tools & Platforms: Dagster, FastAPI, MS Project, Git, Power BI, Matlab, Labview.

• Spanish: Native. English: B2.

EXPERIENCE

• Lead AI, analytics, and automation initiatives focused on improving operational efficiency and enabling data-driven decision-making.

• Coordinate cross-functional collaboration across technical and business stakeholders to support successful project execution and technology adoption.

• Drive end-to-end delivery of AI solutions, from opportunity identification and solution design through deployment and operational integration.

• Develop reporting and visualization tools that improve visibility into technology initiatives and support informed decision-making.

• Support planning, documentation, and continuous improvement efforts that strengthen technology delivery and organizational capabilities.

• Provide technical leadership in machine learning, computer vision, and data-driven solutions while mentoring team members and promoting best practices.

• Developed and deployed computer vision and ML solutions for inspection and production optimization

• Designed and implemented data pipelines using orchestration frameworks, enabling scalable and reliable data workflows

• Built predictive and analytical models to support operational decisions and process improvements

• Delivered proof-of-concepts that contributed to the evaluation and adoption of new technologies in manufacturing

• Collaborated with cross-functional stakeholders to translate business problems into technical solutions

• Evaluation and report of the final defects of the production line. Preparation, correction, and translation of work instructions for operators.

PROJECTS

Resource allocation in wireless networks through Deep Reinforcement Learning

• Accelerated the learning of the conventional Deep Q-Network model for power allocation in wireless networks by up to 77% and improved the network performance by up to 24.7% by proposing different training strategies with transfer learning. Simulations were performed using Python with Pytorch.

• Conceptualized, analyzed, and wrote three published refereed articles in top journals from Q1 and Q2 in the computer science area.

• Conducted a systematic review methodology and identified the 56 most relevant research works implementing machine learning for resource allocation. Performed data extraction, cleaning, and visualization using Excel.

AI Repository Assistant

• Developed an AI chatbot that indexes GitHub repositories and provides context-aware answers to code and documentation using Python.

• Implemented a repository indexer, context-aware search engine, and a conversational AI agent to generate accurate responses in real-time.

• Built a streaming chat interface with Streamlit for an interactive user experience and logged conversations for reproducibility and auditing.

Recognition of eye diseases with neural networks

• Designed a convolution neural network with 89.2% accuracy for detecting ocular diseases using the ODIR-5K database of the Kaggle platform using Python with Keras.

• Managed and planned teamwork tasks for the preprocessing stage involving image formatting, data cleaning, and data augmentation for unbalanced classes.

Explainable AI (XAI) for beer brand classification

• Implemented GRAD-CAM, an explainable AI method, to interpret the Convolutional Neural Network (CNN) decision-making for beer brand classification.

• Fine-tuned the pre-trained VGG16 CNN architecture with additional layers to achieve an accuracy of 91.6%. Data augmentation, preprocessing, and training were performed using libraries such as Keras and Sklearn.

EDUCATION

Universidad Autónoma de Baja California – Mexicali, Baja California, México.

Universidad Autónoma de Baja California – Mexicali, Baja California, México.

Universidad Autónoma de Baja California – Mexicali, Baja California, México.

CERTIFICATIONS

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