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

Verified Expert  in Engineering

Software Developer

Location
Mexico City, Mexico
Toptal Member Since
May 30, 2019

Luis has over a decade of experience developing software using various technologies, including Python and Java. He has worked in engineering and cross-disciplinary teams, creating innovative ML services for one of the largest retail websites in the world and building a first-of-its-kind platform for financial analysts. He has experience working with teams of all sizes using Agile and traditional methodologies.

Portfolio

DevFactory
Amazon Web Services (AWS), Python, TypeScript, Google Cloud Platform (GCP)...
Finsera
Python 3, SQL, ETL, Amazon Web Services (AWS)...
Amazon
Amazon Web Services (AWS), Microservices, Spark, MXNet, Java, Python...

Experience

Availability

Part-time

Preferred Environment

Git, IntelliJ IDEA, PyCharm, Linux, MacOS

The most amazing...

...project I've done was rebuilding a data transformation algorithm that allowed us to reduce the runtime of large data ingestion tasks from weeks to hours.

Work Experience

VP of Technical Product Management

2020 - PRESENT
DevFactory
  • Acted as the software architect and product owner for ESW products. Wrote technical specs defining engineering work and reviewed engineering deliverables to verify value and code quality.
  • Collaborated on developing the CloudFix product, a SaaS service that reviews the usage of AWS resources to identify and execute cost-reduction opportunities.
  • Implemented PoCs and experiments to validate the potential of new technologies, mainly focused on AWS and, recently, on LLMs.
  • Led two developers on the design and implementation of a remote work management and tracking tool for our fully remote, async team.
Technologies: Amazon Web Services (AWS), Python, TypeScript, Google Cloud Platform (GCP), Node.js, GraphQL, Serverless, APIs, JavaScript, Infrastructure as Code (IaC), Cloud Infrastructure, React, Serverless Architecture, AWS CloudFormation, AWS AppSync, Lambda Functions, Amazon Cognito User Pools, WebSockets, PostgreSQL, MySQL, CI/CD Pipelines, Caching, Amazon DynamoDB, Amazon S3 (AWS S3)

Software Engineer

2020 - 2021
Finsera
  • Designed and built our APIs and back-end task manager using Python, FastAPI, and Dramatiq. The efficient design meant adding new API endpoints by adding new types and a wrapper to the back-end implementation.
  • Built Finseras data ingestion service, a data pipeline built on AWS Lambda and Step Functions, written in Python. This service consumes data from sources like Reuters or IBES and allows customers to onboard their custom data in a few clicks.
  • Proposed and built a PoC for replacing our Kubernetes cluster with AWS Lambda. Reduced the max launch time from a worst-case scenario of around two minutes to two seconds.
Technologies: Python 3, SQL, ETL, Amazon Web Services (AWS), Amazon Simple Queue Service (SQS), Pandas, AWS Step Functions, Serverless, APIs, REST APIs, Object-oriented Programming (OOP), JSON, FastAPI, Python, Infrastructure as Code (IaC), Cloud Infrastructure, DevOps, Kubernetes, Serverless Architecture, AWS CloudFormation, Lambda Functions, Docker, PostgreSQL, CI/CD Pipelines, Caching, Amazon DynamoDB, Amazon S3 (AWS S3), Pytest

Software Development Engineer

2017 - 2019
Amazon
  • Collaborated in building a data pipeline for automatically annotating catalog data. The pipeline orchestrated multiple microservices to automatically annotate Amazon's apparel catalog items with meaningful attributes to enhance the search experience.
  • Created a fast annotation web app. It used an ML engine in the back-end to assist marketing or data specialists in labeling very large datasets quickly and with high accuracy.
  • Rewrote an EC2-based prediction service for running inferences based on MXnet to run on AWS Lambda, providing greatly increased TPS for a fraction of the cost.
  • Advocated for and led the team in migrating to IaC and CI/CD pipelines defined in code using Ruby and CloudFormation.
Technologies: Amazon Web Services (AWS), Microservices, Spark, MXNet, Java, Python, Serverless, APIs, REST APIs, Object-oriented Programming (OOP), JavaScript, Infrastructure as Code (IaC), Cloud Infrastructure, DevOps, Serverless Architecture, AWS CloudFormation, Lambda Functions, Flask, Docker, PostgreSQL, CI/CD Pipelines, Caching, Amazon DynamoDB, Amazon S3 (AWS S3), Pytest

Application Developer

2012 - 2016
Oracle
  • 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 codebase.
  • Provided fast resolution of product bugs reported by our customers.
Technologies: SQL, PL/SQL, ADF, Java, Object-oriented Programming (OOP), Spring

Programmer Analyst

2011 - 2012
Oracle
  • Created a web application that allowed customer support teams to self-service and fix their most common problems. This reduced our bug influx and allowed us to respond to more complex issues faster and have the bandwidth to deliver root cause fixes.
  • Created an on-call management and alerting tool that enabled on-call engineers to configure shifts, escalation, and receive alerts on their phones. This greatly simplified on-call for DevOps engineers and their managers.
  • Wrote web services to integrate three disparate ticketing systems into one, reducing the workload for DevOps engineers and the ticket resolution time.
  • Provided debugging support and code fixes for Oracle's internal deployment of quote-to-order enterprise software.
Technologies: SQL, PL/SQL, Java, DevOps

Amazon Fashion

https://www.amazon.com/Dresses/
I collaborated in building a data analysis pipeline that orchestrated multiple services to annotate items in Amazon's apparel catalog automatically. I then automated predictions using MxNet networks and extracted meaningful attributes from catalog images (e.g., floral pattern, scoop neck), which were used to enhance the catalog items, so Amazon customers can use them to search or filter when shopping for clothing.

I was involved in building the prediction services that used MxNet, and a fast-annotation and review service: a web application so data annotators could review low-confidence predictions. Also built a data warehouse to collect metrics throughout the whole process. This was used by our data scientists to improve the existing models and perform new experiments.

On-call Rotation and Alert Manager.

I developed an on-call rotation monitoring tool while working as part of a DevOps team. 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 allow managers to set up escalation rules for high-severity incidents.

Before this, the organization had no tool for managing on-call, so rotation was controlled differently across teams, mostly through shared documents or emails. Setting up alerts was complicated and required engineers to have privileges in systems they usually should not have had access to.

A centralized tool for on-call 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 team quickly became a new internal product, part of the standard tools for the entire organization.

ML-assisted Data Annotation Tool.

Collaborated in developing a web application to allow 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.

CloudFix: Cloud optimization for AWS

https://aws.amazon.com/marketplace/pp/prodview-5wc4rjxznjmwq
CloudFix is a cost-optimization service for AWS. It scans AWS resources in an organization, identifies inefficient resources, and automatically and securely fixes them.

I collaborated in designing the architecture of the CloudFix framework, designing the detection and fixing algorithms, and implementing the product in TypeScript and Node.js.

Languages

Python, JavaScript, Java, SQL, TypeScript, Python 3, GraphQL

Tools

AWS CLI, AWS CloudFormation, Pytest, PyCharm, IntelliJ IDEA, Git, Amazon Simple Queue Service (SQS), AWS Step Functions, AWS AppSync

Platforms

Amazon Web Services (AWS), AWS Lambda, Linux, MacOS, Google Cloud Platform (GCP), Kubernetes, Docker

Storage

Amazon DynamoDB, Amazon S3 (AWS S3), PostgreSQL, MySQL, Oracle SQL, JSON, PL/SQL

Frameworks

Flask, ADF, MXNet, Spark, Bootstrap, Spring, Hibernate

Libraries/APIs

Vue 2, Node.js, REST APIs, React, Pandas

Paradigms

Agile, Microservices, Object-oriented Programming (OOP), Continuous Integration (CI), DevOps, Serverless Architecture, Functional Programming, Object-relational Mapping (ORM), ETL

Other

Software Development, Serverless, APIs, Infrastructure as Code (IaC), Cloud Infrastructure, Lambda Functions, CI/CD Pipelines, Software Design, Machine Learning, Fintech, FastAPI, Amazon Cognito User Pools, WebSockets, Caching

2005 - 2009

Bachelor of Science Degree in Computer Science

ITESM Mexico City - Mexico City, Mexico

FEBRUARY 2021 - FEBRUARY 2024

AWS Solutions Architect Associate

AWS

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