Nov 2025 – Nov 2025 · 0 mo

Oct 2023 – Dec 2025 · 2 yrs 2 mos · Seattle, Washington, United States

• Leveraged GitHub Actions to automate the deployment pipeline for machine learning models, ensuring efficient and consistent delivery across development, staging, and production environments.
• Integrated GitHub Actions with existing CI/CD processes to automate testing, packaging, and deployment of machine learning models, reducing manual intervention and accelerating time to market.
• Managed and monitored 350 ECS tasks across three distinct environments (development, staging, and production), ensuring the optimal performance and availability of machine learning models hosted on AWS ECS cluster

Aug 2023 – Present · 2 yrs 9 mos

• Explorando el mundo de la tecnología junto a 6.000+ amiwos de más de 10 países.

Aug 2022 – Aug 2023 · 1 yr · Seattle, Washington, Estados Unidos

• MLOps: Successfully implemented the machine learning infrastructure for the Transit Time and Service Time predictive services. Leveraging AWS SageMaker, I designed and deployed a highly scalable multi-model endpoint that enabled the services to predict customer delivery times with high accuracy.
• Monitoring: Successfully led the effort to rename StatsD to better leverage Datadog services. My responsibilities included collaborating with cross-functional teams, developing a plan to refactor the codebase, and ensuring seamless migration to the new naming convention.

Aug 2021 – May 2022 · 9 mos · Berkeley, California, Estados Unidos

• Fung Excellence Scholarship Recipient
• Relevant Coursework: Parallel Computing, Experimental Design for Machine Learning on Multimedia Data
• Relevant Projects:
• Political Inclination Prediction Model
• Developed a Logistic Regression model using Scikit learn with a 98.3% accuracy that predicted the political inclination of all of the U.S. counties based on COVID-19 hygiene habits. Used PCA and Correlation Matrix to determine the most relevant variables.
• Analysis Neural Network Models for Chromatin Feature Prediction
• Analyzed the DanQ model that predicts chromatin features directly from raw DNA sequence. Calculated the Memory Equivalent Capacity of the model. Trained the model for generalization, which identified that a vanilla neural network differed only by 2% of accuracy from the original model that contained a CNN and a bidirectional LSTM.

Aug 2021 – Dec 2021 · 4 mos · Berkeley, California, Estados Unidos

• Taught graduate students the fundamentals of data analysis and visual representation of data on Tableau. Guided MBA and MPP students in representing findings through visualizations made in Excel and Tableau.

Aug 2020 – Sep 2021 · 1 yr 1 mo

• Develop scalable, elegant infrastructure as code using Terraform in order to manage and automate multi-platform cloud services to obtain a .0001% downtime for the largest catalog company in Latin America. Responsible for the implementation and maintenance of architecture hosted in GCP. Created Jenkins pipelines to build, tag, and push customized Docker images. Experience in creating and maintaining AWS User Accounts, Security Groups, VPC, Route 53, DynamoDB, RDB, SNS, SQS, and AWS storage services.

May 2019 – May 2020 · 1 yr · Monterrey y alrededores, México

• Represented Mexico and Ennya Beauty in a trade mission in: Perú, Brazil, Argentina, Uruguay, Chile, Colombia and Panama

Aug 2018 – May 2020 · 1 yr 9 mos · Monterrey y alrededores, México

• Developed an API in JavaScript as the back-end for an application that delivered real time location to track employees inside a factory. The API established communication with Cisco API to obtain the latitude and longitude of employees and stored it in MongoDB, it also established communication with MySQL to provide the name, age, and type of worker.

May 2017 – Aug 2017 · 3 mos · Bahía de San Francisco y alrededores, Estados Unidos

• Developed a RESTful Web Service with JAX- RS with an object oriented structure in Java with a client/server architecture. The Web Service used the Twitter DM API to consume and
• produce responses via direct messages to verify the functionality of DM Bots.
• The Web Service was locally deployed in Tomcat and publicly in Heroku.