{
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  "generated": "2026-08-08T18:53:34.373Z",
  "canonical": "https://butkeraites.com/profile.json",
  "name": "Renan Butkeraites",
  "legalName": "Renan Brito Cano Butkeraites",
  "headline": "I turn optimization research into systems that ship.",
  "summary": "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.",
  "role": "Engineering leader · Optimization specialist",
  "currentPositions": [
    {
      "title": "Information Technology Manager",
      "org": "SG Global Group"
    },
    {
      "title": "Director of Technology",
      "org": "Fresh Codes"
    }
  ],
  "location": {
    "locality": "Kennesaw",
    "region": "GA",
    "country": "US",
    "workModel": "Remote, global"
  },
  "languages": [
    "English",
    "Portuguese"
  ],
  "identifiers": {
    "orcid": "0000-0002-5830-0466"
  },
  "links": {
    "email": "rbritobut@gmail.com",
    "github": "https://github.com/butkeraites",
    "linkedin": "https://www.linkedin.com/in/rbritobut",
    "scholar": "https://scholar.google.com/citations?user=Zh48TgYAAAAJ",
    "orcid": "https://orcid.org/0000-0002-5830-0466",
    "x": "https://x.com/renan_but"
  },
  "expertise": {
    "$comment": "Feeds schema.org knowsAbout and the llms.txt summary. Ordered by depth, not breadth.",
    "core": [
      "Robust optimization under uncertainty",
      "Mixed-integer programming",
      "Vehicle routing and scheduling",
      "Metaheuristics",
      "Operations research"
    ],
    "engineering": [
      "Python backends",
      "Distributed cloud architecture (AWS, GCP, Azure)",
      "Data pipelines",
      "LLM/AI product engineering",
      "Technical leadership and mentoring"
    ],
    "solvers": [
      "Gurobi",
      "CPLEX",
      "OR-Tools",
      "HiGHS",
      "SCIP"
    ],
    "languages": [
      "Python",
      "SQL",
      "TypeScript",
      "Rust",
      "Go"
    ]
  },
  "experience": [
    {
      "role": "Information Technology Manager",
      "organization": "SG Global Group",
      "from": "2026",
      "to": null,
      "current": true,
      "description": "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."
    },
    {
      "role": "Director of Technology",
      "organization": "Fresh Codes",
      "from": "2025",
      "to": null,
      "current": true,
      "description": "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."
    },
    {
      "role": "Technical Lead",
      "organization": "Serendipe Institute of Science & Technology",
      "from": "2025",
      "to": "2026",
      "current": false,
      "description": "Drove the institute's technical strategy and R&D execution; project prospecting, technical hiring, and mentoring of engineers and researchers."
    },
    {
      "role": "Founder & CEO",
      "organization": "BSI — AI & Optimization Consulting (closed)",
      "from": "2024",
      "to": "2026",
      "current": false,
      "description": "Founded and led a consulting firm in Campinas, Brazil, crafting bespoke AI strategies and optimization solutions for clients; operations closed in January 2026."
    },
    {
      "role": "Integration Consultant, Backend Python",
      "organization": "hotglue",
      "from": "2024",
      "to": "2025",
      "current": false,
      "description": "Designed and implemented custom data-integration solutions on hotglue's embedded ETL platform."
    },
    {
      "role": "Backend Python Team Lead",
      "organization": "Optibus",
      "from": "2022",
      "to": "2024",
      "current": false,
      "description": "Led the customer-oriented R&D team for specialized exports; pair-programming culture, knowledge-sharing initiatives (\"Optifridays\"), technical interviewing during rapid company growth."
    },
    {
      "role": "Researcher, Optimization",
      "organization": "Nitryx Consulting → Progress Rail (Caterpillar)",
      "from": "2021",
      "to": "2022",
      "current": false,
      "description": "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."
    },
    {
      "role": "Data Science Specialist",
      "organization": "Banco Safra",
      "from": "2020",
      "to": "2021",
      "current": false,
      "description": "Mixed Media Models, Markov-chain behavior models, PIX fraud-pattern detection, executive-level analytics."
    },
    {
      "role": "Senior Data Scientist",
      "organization": "Porto Seguro",
      "from": "2019",
      "to": "2020",
      "current": false,
      "description": "API-based model serving on AWS; optimization heuristics for logistics; ML with XGBoost, scikit-learn, Gurobi/CPLEX."
    },
    {
      "role": "Mathematical Analyst",
      "organization": "UniSoma",
      "from": "2018",
      "to": "2019",
      "current": false,
      "description": "Column-generation scheduling with CPLEX for railroad workforce planning; real-time Big Data (Kafka, PySpark) NLP fraud detection."
    },
    {
      "role": "Research Intern",
      "organization": "CIRRELT — Polytechnique Montréal",
      "from": "2018",
      "to": "2018",
      "current": false,
      "description": "Optimization under uncertainty, supervised by Prof. Michel Gendreau."
    },
    {
      "role": "PhD Candidate, Operations Research",
      "organization": "UNIFESP / ITA (CNPq fellow)",
      "from": "2016",
      "to": "2021",
      "current": false,
      "description": "Thesis: \"Optimization under uncertainty: a new computational method, a new robustness measure, and applications.\""
    }
  ],
  "publications": [
    {
      "title": "A sampling-based multi-objective iterative robust optimization method for the Bandwidth Packing Problem",
      "authors": [
        "Renan Brito Cano Butkeraites",
        "Luiz Leduino de Salles Neto",
        "Michel Gendreau"
      ],
      "venue": "Expert Systems with Applications",
      "year": 2022,
      "doi": "10.1016/j.eswa.2022.117337",
      "url": "https://www.sciencedirect.com/science/article/abs/pii/S0957417422006947",
      "type": "ScholarlyArticle"
    },
    {
      "title": "Forecast UTI: an application for forecasting intensive care unit beds during the COVID-19 pandemic",
      "authors": [
        "Luiz Leduino de Salles Neto",
        "Renan Brito Cano Butkeraites",
        "Martins",
        "Chaves",
        "Horacio Hideki Yanasse"
      ],
      "venue": "Epidemiologia e Serviços de Saúde",
      "year": 2020,
      "doi": null,
      "url": null,
      "type": "ScholarlyArticle"
    },
    {
      "title": "Efficient frontier of credit risk using Monte Carlo simulation",
      "authors": [
        "Renan Brito Cano Butkeraites",
        "José Luiz Chela",
        "Luiz Leduino de Salles Neto"
      ],
      "venue": "International Journal of Business Intelligence and Systems Engineering",
      "year": 2019,
      "doi": null,
      "url": null,
      "type": "ScholarlyArticle"
    },
    {
      "title": "Optimization under uncertainty: a new computational method, a new robustness measure, and applications",
      "authors": [
        "Renan Brito Cano Butkeraites"
      ],
      "venue": "UNIFESP",
      "year": 2021,
      "doi": null,
      "url": "https://repositorio.unifesp.br/handle/11600/63626",
      "type": "Thesis"
    }
  ],
  "projects": [
    {
      "id": "sirom",
      "title": "SIROM",
      "summary": "A sampling-based method that hands you a Pareto frontier of robust solutions and lets you choose the trade-off after seeing the options, instead of committing to an uncertainty budget before you know what it costs.",
      "status": "demo",
      "url": "https://butkeraites.com/projects/sirom/",
      "markdown": "https://butkeraites.com/projects/sirom.md",
      "repository": "https://github.com/butkeraites/sirom",
      "license": "MIT",
      "techniques": [
        "Robust optimization",
        "Monte Carlo sampling",
        "Linear programming",
        "Clustering"
      ],
      "stack": [
        "Python",
        "OR-Tools",
        "scikit-learn",
        "NumPy"
      ],
      "headlineMetrics": [
        {
          "label": "Bandwidth Packing cases matched or beaten",
          "value": "92.5%"
        },
        {
          "label": "Pipeline runtime, after optimization",
          "value": "30.034s → 1.027s (29.2×)"
        },
        {
          "label": "Frontier envelope reproduced across constraint structures",
          "value": "18 / 18"
        }
      ],
      "runsInBrowser": true
    },
    {
      "id": "hardness",
      "title": "Hardness",
      "summary": "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.",
      "status": "demo",
      "url": "https://butkeraites.com/projects/hardness/",
      "markdown": "https://butkeraites.com/projects/hardness.md",
      "repository": "https://github.com/butkeraites/hardness",
      "license": "MIT",
      "techniques": [
        "Robust optimization",
        "Interval uncertainty",
        "Monte Carlo",
        "Formal verification"
      ],
      "stack": [
        "Python",
        "Agda",
        "FastAPI",
        "NumPy"
      ],
      "headlineMetrics": [
        {
          "label": "Measure range",
          "value": "η ∈ [0, 1], continuous"
        },
        {
          "label": "Closed-form evaluation",
          "value": "1.6 µs"
        },
        {
          "label": "20,000-scenario simulation",
          "value": "9 ms"
        },
        {
          "label": "Core theory",
          "value": "machine-checked in Agda"
        }
      ],
      "runsInBrowser": true
    },
    {
      "id": "costas-arrays",
      "title": "The N=32 Wall",
      "summary": "A verified database of Costas arrays for orders 2–100, algebraic generators built over finite fields, and CP/SAT/LP experiments against the smallest orders where nobody has ever found one — or proved none exists.",
      "status": "case-study",
      "url": "https://butkeraites.com/projects/costas-arrays/",
      "markdown": "https://butkeraites.com/projects/costas-arrays.md",
      "repository": "https://github.com/butkeraites/costas-array",
      "license": "MIT",
      "techniques": [
        "Constraint programming",
        "SAT solving",
        "Exhaustive search",
        "Finite fields"
      ],
      "stack": [
        "Python",
        "C++",
        "OR-Tools",
        "kissat"
      ],
      "headlineMetrics": [
        {
          "label": "Arrays verified on every CI run",
          "value": "9,217"
        },
        {
          "label": "Orders with no known array",
          "value": "18, smallest is 32"
        },
        {
          "label": "Exhaustive search, order 15",
          "value": "2.2 s · 743k nodes"
        },
        {
          "label": "Exhaustive search, order 17",
          "value": "does not finish"
        }
      ],
      "runsInBrowser": false
    }
  ],
  "selectedWork": [
    {
      "organization": "Nitryx → Progress Rail · A Caterpillar Company",
      "title": "Vehicle routing engine for Brazil's largest sugar producer",
      "summary": "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.",
      "outcome": "−20% fleet size · $60K/month cloud savings via in-house Distance Matrix APIs",
      "tags": [
        "Python",
        "OR / Routing",
        "Team of 6",
        "Google Maps API"
      ]
    },
    {
      "organization": "Optibus · Public Transit SaaS",
      "title": "Client-facing exports platform for transit operators on 3 continents",
      "summary": "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.",
      "outcome": "−50% new bugs · 2× team development velocity · key contract secured",
      "tags": [
        "Python",
        "AWS",
        "MongoDB",
        "Team Lead"
      ]
    },
    {
      "organization": "PhD Research · UNIFESP + Polytechnique Montréal",
      "title": "SIROM — a robust optimization method under uncertainty",
      "summary": "Created a sampling-based multi-objective iterative method for optimization under uncertainty, plus \"Hardness\", a new robustness measure. Co-authored with Michel Gendreau.",
      "outcome": "Outperformed or matched literature methods in 92.5% of Bandwidth Packing cases",
      "tags": [
        "Robust Optimization",
        "Monte Carlo",
        "Published research"
      ]
    },
    {
      "organization": "Banco Safra · Banking",
      "title": "Behavioral models that moved acquisition and engagement",
      "summary": "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.",
      "outcome": "+15% app activation · +45% high-quality credit card leads",
      "tags": [
        "Python",
        "Markov Chains",
        "MMM",
        "Fraud Analytics"
      ]
    },
    {
      "organization": "Porto Seguro · Insurance",
      "title": "From 5-day to 5-minute model deployments",
      "summary": "Started the company's movement toward API-based models and AWS cloud computing, and shipped a freight-size estimation heuristic used in logistics planning.",
      "outcome": "Deployment time: 5 days → 5 minutes · 20% logistics optimization",
      "tags": [
        "AWS",
        "Flask APIs",
        "Heuristics",
        "ML"
      ]
    },
    {
      "organization": "Public Health · COVID-19",
      "title": "Forecast UTI — ICU bed forecasting during the pandemic",
      "summary": "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.",
      "outcome": "Published in Epidemiologia e Serviços de Saúde (2020)",
      "tags": [
        "Forecasting",
        "Social Impact",
        "Open Source"
      ]
    }
  ]
}