Arjaan Buijk, Machine Learning Developer in Plymouth, MI, United States
Arjaan Buijk

Machine Learning Developer in Plymouth, MI, United States

Member since December 19, 2017
For over 30 years, Arjaan's been developing software and deploying solutions. His expertise lies in Python, deep learning, NLP, chatbots, and developing software for both desktop applications and cloud deployments. Arjaan has a master's degree in aerospace engineering and certifications in deep learning, Kubernetes, self-driving cars, and full-stack web development.
Arjaan is now available for hire

Portfolio

Experience

Location

Plymouth, MI, United States

Availability

Part-time

Preferred Environment

Python

The most amazing...

...project I've recently completed was teaching a Kubernetes deployment workshop at Udemy.

Employment

  • Solutions Engineer (NLP)

    2019 - PRESENT
    Rasa Technologies, Inc.
    • Supported enterprise customers by implementing and deploying mission-critical chatbots built with Rasa.
    • Designed and implemented NLU data, dialog stories, forms, and custom actions (Python) for a demonstration chatbot based on Rasa Open Source.
    • Extended Rasa Open Source (Python) available at https://github.com/rasaHQ/rasa. This is an open-source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more.
    Technologies: TensorFlow, Ubuntu, Windows, DevOps Engineer, Pandas, GitHub, NumPy, Chatbots, Google Cloud Platform (GCP), Helm, Kubernetes, Docker, Python, Rasa.ai, Machine Learning, Natural Language Processing (NLP)
  • Freelance Data Scientist

    2018 - 2019
    Colorado University Boulder | Office of Data Analytics
    • Developed a sequence-based machine learning model in Python using TensorFlow to predict university student application probability based on millions of time-stamped engagements.
    • Developed a clustering logic in Python using scikit-learn to group students by engagement behaviors.
    • Built a decision tree model in Python using XGBoost to predict the probability for admitted students to enroll (yield).
    Technologies: Ubuntu, Windows, Keras, GitHub, NumPy, Python, Slack, Zoom, Jira, Bitbucket, SQL, MongoDB, AWS S3, Jupyter, Sklearn, XGBoost, TensorFlow, Machine Learning, AWS, Amazon Web Services (AWS)
  • Software Engineer

    1988 - 2019
    PISCES | MSC Software | Simufact Engineering
    • Developed a finite element and finite volume simulation software in Python, C++, and Fortran.
    • Designed and implemented a desktop application front-end with the Microsoft Foundation Class Library (MFC) and Qt.
    • Performed pre-sales demonstrations, customer training and support, sales, and business development.
    • Managed a team of solver developers. I was responsible for the definition and execution of projects, yearly employee reviews, and career planning of the direct reports.
    Technologies: Qt, PyQt, Ubuntu, Windows, Microsoft Foundation Class Library (MFC), GUI, Fortran, C++, Python
  • Founder | Owner

    2008 - 2014
    Simufact-Americas, LLC
    • Founded a company for the resale of simulation software that I co-developed.
    • Achieved a 20-fold increase in revenue for the Americas region.
    • Used Python to automate business processes; for example, reformatting the Salesforce forecast reports.
    Technologies: Qt, PyQt, Fortran, Windows, Python

Experience

  • Student Application Prediction (Development)

    I developed a data pipeline and a machine learning model to predict university student application probability based on time-stamped engagements.

    The data pipeline extracted millions of records from several SQL databases and generated features for the machine learning model. The end result of the data-pipeline was a pandas DataFrame written to an S3 bucket.

    The machine learning pipeline loaded the pandas DataFrame and trained a custom TensorFlow model used by the university admissions department to identify the most promising prospective applicants.

  • Rasa Demonstration Chatbot on GCP (Development)
    https://github.com/RasaHQ/rasa-demo

    I designed and implemented NLU data, dialog stories, forms, and custom actions (Python) for a demonstration chatbot, based on Rasa Open Source. The chatbot was trained and deployed on the Google Cloud Platform (GCP).

  • Kubernetes on GCP (Development)
    https://www.udemy.com/course/rasa-advanced-deployment-workshop/

    I created and taught a Rasa advanced deployment workshop, which is hosted on Udemy.

    The workshop teaches how to deploy a Rasa assistant on Kubernetes in the Google Cloud Platform (GCP) and how to use a CI/CD pipeline with GitHub Actions.

  • Chatbot for an Expert System (Development)

    I developed a chatbot with Rasa and Elasticsearch and deployed it to a single node Kubernetes cluster on AWS.

    The chatbot provides an alternative interface to a web-based expert system.

    The data pipeline creates word/sentence embeddings from web-scraped data and injects them into an Elasticsearch database. The embeddings are created using pre-trained machine learning models from TensorFlow Hub.

    The chatbot listens to questions from users and finds the most similar result by querying Elasticsearch.

  • Front-end Design and Implementation of a Windows Desktop Application (Development)
    https://www.mscsoftware.com/assets/2221_SF303ZZZLTDAT.pdf

    An industrially proven software package for the computer simulation of industrial forging processes. It combined a familiar and intuitive Windows graphical user interface with a robust solution procedure to provide unprecedented accuracy and speed in forging simulations.

    I was both a solver and a front-end developer.

  • Use of Deep Learning to Clone Driving Behavior (Development)
    https://github.com/ArjaanBuijk/CarND_Behavioral_Cloning_P3/blob/master/README.md

    I developed a convolutional neural network regression in Python using Keras with a TensorFlow back end to drive a car virtually around a track. I improved on the well-known NVIDIA neural network for end-to-end deep learning for self-driving cars and achieved a 100% success rate on the most difficult track.

  • Flask Catalog (Development)
    https://flask-catalog.herokuapp.com/

    This portfolio project demonstrates a Flask-based implementation of a secure user model with registration, email confirmation, and Google OAuth 2 login, combined with an example of a catalog of items that are grouped in categories.

    Registered users can CRUD the catalog via a web front-end or via a REST API that adheres strictly to the JSON API 1.0 specification. The REST API supports advanced searching and filtering. End-to-end scenarios are demonstrated via a Python client written in a Jupyter notebook.

Skills

  • Languages

    Python, C++, SQL, Fortran
  • Libraries/APIs

    NumPy, Keras, Pandas, Microsoft Foundation Class Library (MFC), Sklearn, TensorFlow, Matplotlib, XGBoost, PyQt, OpenCV
  • Tools

    Rasa.ai, GitHub, Wingware IDE, Google Kubernetes Engine (GKE), Helm, GitLab, Jira, Jupyter, Bitbucket, Zoom, Slack, AWS Glue
  • Platforms

    Jupyter Notebook, MacOS, Kubernetes, Ubuntu, Amazon Web Services (AWS), Windows, Linux, OpenShift, Docker, Google Cloud Platform (GCP), Heroku
  • Other

    Chatbots, Natural Language Processing (NLP), GitKraken, Machine Learning, DevOps Engineer, AWS, Sanic Web Server, Rasa, GitHub Actions, TensorFlow Hub, GUI
  • Storage

    Elasticsearch, Google Cloud, Data Pipelines, AWS S3, MongoDB, PostgreSQL
  • Frameworks

    Qt, Flask

Education

  • Master's degree in Aerospace Engineering (CFD)
    1982 - 1988
    Delft University of Technology - Delft, the Netherlands

Certifications

  • Google Kubernetes Engine
    JANUARY 2020 - PRESENT
    Google via Coursera
  • Deep Learning
    JANUARY 2019 - PRESENT
    Deeplearning.ai via Coursera
  • MongoDB for Developers
    SEPTEMBER 2018 - PRESENT
    MongoDB University
  • Nanodegree in Full-stack Web Development
    MAY 2018 - PRESENT
    Udacity
  • Nanodegree in Self-driving Car Engineer
    DECEMBER 2017 - PRESENT
    Udacity
  • Retrieving, Processing, and Visualizing Data with Python
    JANUARY 2016 - PRESENT
    Coursera

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