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Achievements

Achievement: YOLOAchievement: Pair ExtraordinaireAchievement: Pull Sharkx2Achievement: Quickdraw

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migsr22/README.md

Miguel Rocha

Forward Deployed Engineer, Generative AI

Website · LinkedIn · Kaggle · Email

About

Forward Deployed Engineer specializing in Generative AI, machine learning, and cloud systems. I work alongside engineering and product teams to architect and ship production AI systems, including LLM workflows, retrieval-augmented generation (RAG), and multimodal applications.

Previously, I held applied science, machine learning, and data science roles at Microsoft, IBM, and Toyota.

Experience

Company Role Period
Google Forward Deployed Engineer, 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

  • Production Generative AI & LLM integration
  • Retrieval-augmented generation (RAG) & search
  • Multimodal systems & agentic workflows
  • Model deployment, evaluation, and latency optimization
  • Scalable cloud architecture across Google Cloud, Azure, and AWS

Tech Stack

Languages: Python, SQL, PySpark, Scala, R
AI/ML: TensorFlow, PyTorch, scikit-learn, Hugging Face, LangChain, LlamaIndex
Cloud & Infrastructure: Google Cloud, AWS, Azure, Docker, Kubernetes, Ray, Triton

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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