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migsr22/README.md

Miguel Rocha

Solutions Architect, Generative AI

Website · LinkedIn · Kaggle · Email

About

I’m a Solutions Architect focused on Generative AI, machine learning, and cloud architecture. I work with teams to design and deploy practical AI systems, including LLM applications, retrieval-augmented generation, multimodal workflows, and production-ready ML solutions.

Previously, I worked across applied science, machine learning engineering, and data science roles at Microsoft, IBM, and Toyota.

Experience

Company Role Period
Google Solutions Architect, Generative AI 2024 – Present
Microsoft Applied Scientist, Cloud & AI 2021 – 2023
IBM Data Scientist / ML Engineer 2019 – 2021
Toyota Data Scientist 2018 – 2019

Focus Areas

  • Generative AI and LLM applications
  • Retrieval-augmented generation
  • Multimodal AI systems
  • ML model deployment and evaluation
  • Cloud architecture on Google Cloud, Azure, and AWS

Tech Stack

Languages: Python, SQL, PySpark, Scala, R
AI/ML: TensorFlow, PyTorch, scikit-learn, Keras, NLP
Cloud & DevOps: Google Cloud, AWS, Azure, Docker, Kubernetes

GitHub Stats

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  1. Employee-Attrition Employee-Attrition Public

    Identifying whether an employee will leave the company based on an employee's attributes. I used ML algorithms such as Random Forest, Logistic Regression, Naive Bayes, and LightGBM

    Jupyter Notebook

  2. Kaggle-Nomad2018 Kaggle-Nomad2018 Public

    I placed top 16% in the world on Kaggle in this competition. I used keras and tensorflow for deep learning and several ensembles with least correlated estimates to optimize for accuracy.

    Jupyter Notebook 1

  3. Predicting-Breast-Cancer Predicting-Breast-Cancer Public

    Given a breast tumor's attributes, I used several machine learning models to predict whether if the tumor is malignant or benign

    Jupyter Notebook

  4. House-Prices House-Prices Public

    I used Random Forest to predict a house prices given the house's attributes

    Jupyter Notebook