Cesar Rodriguez, Developer in Berlin, Germany
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Cesar Rodriguez

Verified Expert  in Engineering

Machine Learning Developer

Berlin, Germany

Toptal member since February 22, 2016

Bio

Cesar is a full-stack data scientist with a solid background in software engineering. His experience in machine learning encompasses diverse projects, including NLP, Q&A systems, device identification through signal analysis, algo-trading, and spam detection. He is proficient in Python and has hands-on experience with technologies like Kubernetes, Hugging Face, Pandas, Metaflow, SQL, and RabbitMQ, as well as cloud services such as AWS and Azure.

Portfolio

Deep Neuron Lab
Python, Kubernetes, Hugging Face, Amazon SageMaker, Azure...
Frequenz
Python, Pandas, Redis, Kubernetes, MLflow, PyTorch, Git
Veoo
Python, Redis, RabbitMQ, Kubernetes, Go, Git

Experience

  • Python - 6 years
  • SQL - 4 years
  • Go - 4 years
  • HTML - 3 years
  • Docker - 3 years
  • Kubernetes - 2 years
  • RabbitMQ - 2 years
  • Machine Learning - 2 years

Availability

Part-time

Preferred Environment

OS X, Unix

The most amazing...

...thing I've created was a Q&A system using different machine learning techniques to provide answers in financial documents.

Work Experience

Senior Data Scientist

2022 - PRESENT
Deep Neuron Lab
  • Implemented the complete ML lifecycle for a Q&A system tailored for financial auditing documents. This system accurately predicts answers while minimizing incorrect responses.
  • Trained a language model on 2 GB of financial documents in German to be used as a foundation model in the Q&A downstream task.
  • Led a team of data scientists in the development of a continuous pipeline for structuring financial tables into XBRL positions. These tables were scraped and processed to ensure accuracy and efficiency.
  • Researched and trained models such as LayoutLM combining text and image features that can predict the structure of tabular data based on a table image.
Technologies: Python, Kubernetes, Hugging Face, Amazon SageMaker, Azure, Large Language Models (LLMs), Azure Functions, Git

Data Scientist

2019 - 2021
Frequenz
  • Implemented an NILM system for detecting devices using machine learning by analyzing changes in voltage and current coming from a smart meter.
  • Deployed the NILM system to a Kubernetes cluster as a live app serving the model with MLflow and monitoring it with InfluxDB and Grafana.
  • Built a sequence prediction framework with Ray/Tune capable of testing different models for a given time-series forecasting while also doing hyperparameter optimization.
  • Designed and implemented an algorithmic trader using a combination of sequence prediction models and technical indicators and built a backtesting framework to validate its performance according to profitability.
Technologies: Python, Pandas, Redis, Kubernetes, MLflow, PyTorch, Git

Software Engineer

2017 - 2019
Veoo
  • Built a SMPP server from scratch with internal business logic using Golang, RabbitMQ, and Redis for communication between microservices.
  • Implemented a real-time spam detection system using Python and scikit-learn.
  • Deployed the system in AWS using Docker, Kubernetes and ECS.
Technologies: Python, Redis, RabbitMQ, Kubernetes, Go, Git

Software Engineer II

2013 - 2015
Ooyala
  • Designed and implemented an eCommerce API that handles subscriptions, purchases, and entitlements using Golang and Gorilla web frameworks.
  • Reworked a video ingestion system as a web service using Ruby, Sinatra, and MongoDB. Implemented its front end using JavaScript and AngularJS.
  • Created a manager-worker system using Docker and Ruby which parallelized the ingestion of videos. It was built so that also different types of jobs could be executed.
  • Implemented automated integration tests of the eCommerce API using Selenium.
  • Built a synchronization system in Golang used to ensure consistency across two data stores.
Technologies: Docker, AngularJS, Public Health, Apache ZooKeeper, MongoDB, JavaScript, Python, Ruby, Go, Git

Software Developer Engineer Intern

2013 - 2013
Amazon
  • Provided an in-depth analysis of potential relation and non-relational database candidates to be considered in a database migration.
  • Rebuilt the data access object (DAO) layer to JDBC in order to alleviate memory consumption issues.
  • Wrote the Python script which would perform the database migration.
  • Made an analysis of several security system monitors.
  • Created a platform of monitors using the live data streams coming from security systems.
Technologies: MySQL, JDBC, Python, Java

Co-founder, Back-end Developer

2013 - 2013
GuideBuddy
  • Developed a reservation system for scheduling tours by guides to tourists.
  • Used Twilio SMS API as a part of the verification process of a user.
  • Implemented the integration test framework for verifications using Selenium.
  • Developed the integration of Elasticsearch with Django that allowed users to search the guide database.
  • Created a map search feature where Google Maps were used to display available users by location.
Technologies: Elasticsearch, Twilio, Selenium, MySQL, Django, Python

Undergraduate Researcher

2011 - 2013
Texas A&M University
  • Designed and built scalable parallel algorithms in C++ using an in-house parallel library called STAPL.
  • Implemented novel motion planning strategies which would be later published in scientific journals.
  • Created a 3D representation of an academic building to be used in a motion planning simulation.
  • Performed exhaustive tests of the parallel strategies using node clusters.
Technologies: C++

Experience

Xml2Go

https://github.com/cesar0094/xml2go
A Ruby script that generates Go structs based on a given XML. It manually generates the Go structs with their respective attribute tags is bothersome, slow, and prone to errors. With this script, a person can take a sample XML and obtain the equivalent Go struct. Useful when using SOAP APIs.

Education

2015 - 2019

Master's Degree in Machine Learning and Data Analysis

University of Helsinki - Helsinki

2009 - 2013

Computer Engineering Degree in Computer Science

Texas A&M University - College Station

Certifications

MAY 2019 - PRESENT

Structuring Machine Learning Projects

Coursera

MAY 2019 - PRESENT

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Coursera

MAY 2019 - PRESENT

Neural Networks and Deep Learning

Coursera

Skills

Libraries/APIs

NumPy, JDBC, Twilio API, Pandas, PyTorch

Tools

RabbitMQ, Gorilla, Git, Apache ZooKeeper, Amazon SageMaker

Languages

Python, Go, SQL, Ruby, C++, CSS, Java, JavaScript, HTML

Frameworks

Django, AngularJS, Flask, Selenium, Ruby on Rails (RoR), Sinatra

Paradigms

Distributed Computing, Parallel Computing, REST, Agile Software Development

Platforms

Docker, Kubernetes, Unix, OS X, Twilio, MacOS, Linux, Azure, Azure Functions

Storage

MongoDB, Redis, Elasticsearch, MySQL

Other

Machine Learning, Neural Networks, Public Health, MLflow, Hugging Face, Large Language Models (LLMs)

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