AI Engineer · ML Architect · Open Source
I build AI and data products and platforms, from architecture to production
I design and ship complete systems: architecture, data, models, and product. Creator of Ciaren, a visual workflow builder for data and machine learning pipelines, and maintainer of open-source Python libraries used by data teams worldwide.
8+
Years building ML systems
19.4K+
Monthly PyPI downloads
467+
GitHub stars
Flagship product
v0.3 Alpha
Ciaren
I build data pipelines on a visual canvas, previewing every step on real data and exporting production-ready Python. Local-first, open core, and extensible through a plugin system.
· Drag-and-drop library with 80 built-in nodes
· Real-time preview on real data
· Imports Python and keeps execution local
· Runs locally or in your own cloud
· Plugin system for custom transformations
· Marketplace for processing flows
Ecosystem
Products, open source, and the knowledge behind them
Products in active development, open-source libraries, and technical content that connect everything I build.
Products
Open Source Libraries
Technical Content
Open Source
Open source, proven by the community
Python libraries with years of maintenance, steady releases, and adoption you can verify: public code, documentation, and downloads.
Open source only
sklearn-genetic-opt
A scikit-learn-compatible library for hyperparameter tuning and feature selection powered by evolutionary algorithms. A drop-in replacement for GridSearchCV and RandomizedSearchCV: finds optimal parameters faster across any estimator, pipeline, or callable. Actively maintained since 2021, published on PyPI and conda-forge, and used in production ML pipelines by teams worldwide.
467 stars
19.4K downloads/mo
123 documentation
Pyworkforce
A Python library for workforce management optimization: Erlang-C queue modeling for call center staffing, LP-based shift scheduling, and multi-skill rostering. Built for operations teams that need rigorous mathematical solutions, not heuristics.
Iterixs
An interactive learning platform for Data Science and Machine Learning: notebooks and SQL that run entirely in the browser, a skill graph that shows exactly what to learn next, guided learning paths toward data roles, and spaced-repetition review. Currently in private development.
Impact
Open source impact
Live metrics from the libraries, documentation, and content I maintain.
19.4K+
Monthly downloads
123+
Documentation pages
15+
Technical articles
Latest release: sklearn-genetic-opt v0.11.2 · Jun 2026
Blog
Technical Articles
Writing on AI, ML engineering, and open-source development.
Jun 2026 · LLMs & Agents
Structured Data Extraction from Text with LLMs
Use Python, Pydantic, and the Instructor library to extract structured data from unstructured text. Parse invoices and job postings into typed, validated Python objects reliably and at scale.
Read →
Jun 2026 · LLMs & Agents
AI Agents with LangGraph for Data Analysis
Build an autonomous data analysis agent using LangGraph. Learn how agents differ from RAG, and implement the ReAct loop with tools for querying and summarizing data.
Read →
Jun 2026 · MLOps
Orchestrate ML Pipelines with Prefect
Go from fragile training scripts to robust, observable ML workflows using Prefect. Schedule retraining, handle failures gracefully, and monitor every run.
Read →Community
Where I share code, writing, and discussion
GitHub
Code and repositories
Medium
Technical essays
Professional updates
Documentation
Project guides
Tutorials
Step-by-step guides
The person behind it
Who builds this
Hi, I'm Rodrigo
Software Architect & Machine Learning Engineer
The person behind Ciaren, sklearn-genetic-opt, PyWorkforce, and Iterixs: designing and building products and platforms for AI and data, and shipping production ML systems.
Everything I build starts as a real problem from production systems: a hyperparameter search that never finishes, a scheduling nightmare, a pipeline nobody can maintain. Some solutions become open-source libraries; the bigger ones become products.
The rule across this site is the same one I work by: show the work. Code, releases, documentation, and running software over claims.
Engineering
Architecture and product engineering
For companies building something substantial: AI platforms, data infrastructure, and complete products.
AI platforms
LLM, RAG, and agent systems designed for production, with evaluation, cost, and reliability solved.
Data infrastructure
Pipelines, streaming, and data platforms that scale: the foundation every AI system stands on.
ML systems and MLOps
Training, deployment, monitoring, and retraining loops that keep models useful after launch.
Platform architecture
Services, plugins, APIs, and the trade-offs that decide how far a product can go.
Applied optimization
AutoML, evolutionary algorithms, forecasting, and scheduling: the specialty behind my libraries.
Developer tooling
Internal tools, documentation, and developer experience that multiply an engineering team.
Building an AI or data platform?
I design and build critical systems end to end: architecture, data, models, and product. If that is the scale you are working at, let us talk.