Luis Miguel Benitez Ruiz, AWS Developer in Mexico City, Mexico
Luis Miguel Benitez Ruiz

AWS Developer in Mexico City, Mexico

Member since November 24, 2018
Luis has almost a decade of experience developing software using a variety of technologies including Python and Java. For the last few years, he has worked side by side with data scientists and created innovative services that enabled other teams to create first of its kind features for one of the largest retail websites in the world. He has experience working with teams of all sizes, using both Agile and traditional methodologies.
Luis is now available for hire




Mexico City, Mexico



Preferred Environment

macOS, Linux, PyCharm, IntelliJ, Git

The most amazing...

...project I've done was rewriting a service for running online MXNet predictions using serverless technology, achieving 10x the TPS for 15% of the cost.


  • Software Development Engineer

    2017 - 2019
    • Participated in migrating a big data pipeline from a cumbersome script-based setup into an automated system based on independent services.
    • Created a web application that used online machine learning for fast labeling of very large datasets.
    • Rewrote a service in charge of providing on-demand MXnet predictions using serverless technology, improving TPS and reducing cost.
    • Wrote data integrations which allowed richer, meaningful attributes to be indexed in the retail website for apparel.
    Technologies: Python, Java, MXnet, AWS, Spark
  • Application Developer

    2012 - 2016
    • Developed new features for Oracle's Fusion Incentive Compensation product.
    • Coded unit, integration, and end-to-end tests to improve the quality of the existing code base.
    • Provided fast resolution of product bugs reported by our customers.
    Technologies: Java, ADF, PL/SQL, SQL
  • Programmer Analyst

    2011 - 2012
    • Created a web application that allowed customer support teams to diagnose and fix their most common problems in a self-service manner.
    • Created a web application where support teams could manage their on-call schedules and email/SMS alerts.
    • Wrote web services to integrate three disparate ticketing systems into a single one.
    • Provided debugging support and code fixes for Oracle's internal deployment of quote-to-order enterprise software.
    Technologies: Java, PL/SQL, SQL


  • Amazon Fashion (Development)

    Our team built services and infrastructure that allow every new dress added to the site to be searchable by attributes such as "Sleeve Type" or "Neckline" with no human intervention.

  • On-call Rotation and Alert Manager. (Development)

    While working as part of a DevOps team, I developed an on-call rotation monitoring tool. This app monitored our bug tracking databases and allowed engineers to create on-call shifts for their chosen time frames and products; set up alerts to their email and mobile number; and also allowed managers to set up escalation rules for high severity incidents.

    Before this, the organization did not have any tool for managing on-call, so rotation was managed differently across teams, mostly through shared documents or emails. Setting up alerts was complicated and required engineers to have privileges in systems where they normally should not have had access to.

    Having a centralized tool for on-call made gave better visibility and accountability to the on-call rotation for all teams, made the onboarding process easy and quick for new team members, and helped keep sensitive systems more secure.

    It was so well received that what began as a personal project for my own team, quickly became a new internal product, part of the standard tools for the entire organization.

  • ML-assisted Data Annotation Tool. (Development)

    I worked with a team developing a web application that allowed the fast manual labeling of very large data sets, through the use of online machine learning.

    The application iteratively built classification models and generated quality metrics as the user provided labeled data. The annotator had a very streamlined and simple UX, and data scientists had access to aggregated metrics and other metadata for the generated models.

    With this tool, annotators had to label only from 1% to 4% of the samples in a dataset, while before they had to manually label 100% of the dataset. This allowed a much quicker turnaround when generating good quality labeled datasets for our data science team.


  • Languages

    Python, Java, JavaScript, SQL
  • Tools

  • Platforms

    AWS Lambda, Linux, MacOS
  • Storage

    AWS DynamoDB, PostgreSQL, Oracle SQL, MySQL
  • Frameworks

    Flask, Bootstrap
  • Libraries/APIs

    Vue.js 2, React
  • Paradigms

    Agile, Microservices, Functional Programming, Object-relational Mapping (ORM)
  • Other

    Software Development, AWS


  • Bachelor of Science degree in Computer Science
    2005 - 2009
    ITESM Mexico City - Mexico City, Mexico

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