Cesar is available for hire
Hire CesarCesar 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/xml2goA 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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