Engineering leader · Optimization specialist
Engineering leader with a PhD in Operations Research and 7+ years building Python backends, AI/LLM products, and decision-support systems — from mathematical model to production.
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Robust optimization
A continuous, normalized robustness measure for optimization under interval uncertainty — one number in [0,1] that ranks feasible solutions by how well they resist the uncertainty around them, with an exact closed form and a Monte-Carlo estimator that agree.
Measure range: η ∈ [0, 1], continuous
Outcomes, not job descriptions. Every number below is one I owned or led.
Nitryx → Progress Rail · A Caterpillar Company
Designed and led a Python optimization engine for fleet routing across 40k+ locations — started at Nitryx, carried through Progress Rail's acquisition, and taken from concept to production-ready in 16 months with a team of 6.
−20% fleet size · $60K/month cloud savings via in-house Distance Matrix APIs
Optibus · Public Transit SaaS
Led a 5-developer team owning report generation and integrations for clients across North America, Latin America, and Western Europe — including real-time transportation map feeds.
−50% new bugs · 2× team development velocity · key contract secured
PhD Research · UNIFESP + Polytechnique Montréal
Created a sampling-based multi-objective iterative method for optimization under uncertainty, plus "Hardness", a new robustness measure. Co-authored with Michel Gendreau.
Outperformed or matched literature methods in 92.5% of Bandwidth Packing cases
Banco Safra · Banking
Built a Markov-chain model of app user behavior to retarget communication, and executive-level analyses that pivoted credit policy and marketing. Led fraud-pattern analysis on PIX instant payments.
+15% app activation · +45% high-quality credit card leads
Porto Seguro · Insurance
Started the company's movement toward API-based models and AWS cloud computing, and shipped a freight-size estimation heuristic used in logistics planning.
Deployment time: 5 days → 5 minutes · 20% logistics optimization
Public Health · COVID-19
Co-built an application forecasting intensive-care bed demand for Brazilian health services during COVID-19, published in a national epidemiology journal. Also open-sourced a classroom-occupancy optimizer for safe school distancing.
Published in Epidemiologia e Serviços de Saúde (2020)
Ten years from mechatronics technician to engineering leadership.
Leading IT operations and technology strategy for SG LAW LLP (Kennesaw, GA): LLM-powered legal solutions for research, contract review, and document analysis; governance of confidential legal datasets (anonymization, redaction, privacy compliance); cybersecurity across AI systems; firm-wide AI adoption and training.
End-to-end backend engineering for AI-driven product development: distributed cloud architectures (AWS, Azure), high-throughput data pipelines, LLM-powered ML models. Mentoring a fully remote team of 4+ engineers.
Drove the institute's technical strategy and R&D execution; project prospecting, technical hiring, and mentoring of engineers and researchers.
Founded and led a consulting firm in Campinas, Brazil, crafting bespoke AI strategies and optimization solutions for clients; operations closed in January 2026.
Designed and implemented custom data-integration solutions on hotglue's embedded ETL platform.
Led the customer-oriented R&D team for specialized exports; pair-programming culture, knowledge-sharing initiatives ("Optifridays"), technical interviewing during rapid company growth.
Started the vehicle-routing optimization engine at Nitryx and carried it into Progress Rail after the acquisition — leading a team of 6 from conception to production-ready in 16 months, with Distance Matrix APIs and six real-time integrations.
Mixed Media Models, Markov-chain behavior models, PIX fraud-pattern detection, executive-level analytics.
API-based model serving on AWS; optimization heuristics for logistics; ML with XGBoost, scikit-learn, Gurobi/CPLEX.
Column-generation scheduling with CPLEX for railroad workforce planning; real-time Big Data (Kafka, PySpark) NLP fraud detection.
Optimization under uncertainty, supervised by Prof. Michel Gendreau.
Thesis: "Optimization under uncertainty: a new computational method, a new robustness measure, and applications."
Peer-reviewed work at the intersection of optimization and uncertainty.
Renan Brito Cano Butkeraites, Luiz Leduino de Salles Neto, Michel Gendreau — Expert Systems with Applications, vol. 203, 2022 · doi:10.1016/j.eswa.2022.117337
Forecast UTI: an application for forecasting intensive care unit beds during the COVID-19 pandemic
Luiz Leduino de Salles Neto, Renan Brito Cano Butkeraites, Martins, Chaves, Horacio Hideki Yanasse — Epidemiologia e Serviços de Saúde, 2020
Efficient frontier of credit risk using Monte Carlo simulation
Renan Brito Cano Butkeraites, José Luiz Chela, Luiz Leduino de Salles Neto — International Journal of Business Intelligence and Systems Engineering, 2019
Renan Brito Cano Butkeraites — UNIFESP, 2021
The short version of a long path.
I started as a mechatronics technician maintaining flight simulators, and ended up with a PhD in Operations Research — because I kept asking how decisions could be made better under uncertainty. Along the way I did research with Michel Gendreau in Montréal, published the SIROM method, and created "Hardness", a new way to measure how robust a solution really is.
Since then I've applied that lens in insurance, banking, rail logistics, public transit, and data platforms: always the same job, turning a messy real-world decision into a model, and the model into production software. I've led teams through that whole arc — hiring, mentoring, pair programming, and shipping.
Today I manage technology for SG Global Group in Kennesaw, Georgia — leading legal AI and LLM initiatives for SG LAW LLP — while directing technology at Fresh Codes, an AI-driven product studio, with remote teams and global clients. Off the clock: mathematical puzzles, good food, and music — sometimes all three while putting my daughter to sleep.