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


  • Rasa
    TensorFlow, Ubuntu, Windows, DevOps, Pandas, GitHub, NumPy, Chatbots...
  • University of Colorado Boulder
    Ubuntu, Windows, Keras, GitHub, NumPy, Python, Slack, Zoom, Jira, Bitbucket...
  • MSC Software
    Qt, PyQt, Ubuntu, Windows, Microsoft Foundation Class Library (MFC), GUI...



Plymouth, MI, United States



Preferred Environment


The most amazing...

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


  • Solutions Engineer (NLP)

    2019 - PRESENT
    • Supported large enterprise customers by implementing and deploying mission-critical chatbots built with Rasa. Deployments use docker, docker-compose, kubernetes and openshift. Infrastructure a combination of on-prem, AWS, GCP, Azure.
    • Designed and implemented NLU data, dialog stories, rules, forms, and custom actions (Python) for industry-relevant demonstrator chatbots.
    • Extended Rasa Open Source (Python) available at This is an open-source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more.
    • Created & taught an online course on advanced deployment techniques with Kubernetes (
    • Implemented Python Asyncio in back-end APIs resulting in dramatically improved throughput rates.
    • Created CI/CD pipeline that trains a Rasa bot, builds a custom docker image, stores the artifacts in AWS S3 and AWS ECR, automatically creates an AWS EKS cluster using the eksctl cli, deploys Rasa with helm, and smoketests using python.
    Technologies: TensorFlow, Ubuntu, Windows, DevOps, Pandas, GitHub, NumPy, Chatbots, Google Cloud Platform (GCP), Helm, Kubernetes, Docker, Python, Machine Learning, Natural Language Processing (NLP), AWS, CI/CD Pipelines, GitHub Actions, Rasa, CircleCI, Python Asyncio, PostgreSQL
  • Freelance Data Scientist

    2018 - 2019
    University of Colorado Boulder
    • 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
  • Software Engineer

    1988 - 2019
    MSC Software
    • 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 manufacturing simulation software that I co-developed.
    • Achieved a 20-fold increase in revenue for the Americas region.
    • Used Python and web development to automate business processes.
    • Created pre-sales, sales, and post-sales onboarding processes.
    Technologies: Qt, PyQt, Fortran, Windows, Python, Django


  • 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)

    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)

    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 and 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)

    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)

    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)

    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.


  • Languages

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

    NumPy, Keras, Pandas, Microsoft Foundation Class Library (MFC), Sklearn, TensorFlow, Matplotlib, Python Asyncio, XGBoost, PyQt, OpenCV
  • Tools, GitHub, Wingware IDE, Google Kubernetes Engine (GKE), Helm, GitLab, AWS CloudFormation, Jira, Jupyter, Bitbucket, Zoom, Slack, AWS Glue, CircleCI, Ansible
  • Platforms

    Docker, Jupyter Notebook, MacOS, Kubernetes, Ubuntu, Google Cloud Platform (GCP), Amazon Web Services (AWS), Heroku, Windows, Linux, AWS EC2, OpenShift
  • Other

    Chatbots, Natural Language Processing (NLP), GitKraken, AWS, Machine Learning, CI/CD Pipelines, AWS EKS, AWS VPC, Sanic Web Server, Rasa, GitHub Actions, TensorFlow Hub, GUI, FastAPI
  • Paradigms

  • Storage

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

    Qt, Flask, Django


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


  • Nanodegree, Cloud DevOps Engineer
    MARCH 2021 - PRESENT
  • Google Kubernetes Engine
    Google via Coursera
  • Deep Learning
    JANUARY 2019 - PRESENT via Coursera
  • MongoDB for Developers
    MongoDB University
  • Nanodegree, Full-stack Web Development
    MAY 2018 - PRESENT
  • Nanodegree, Self-driving Car Engineer
  • Retrieving, Processing, and Visualizing Data with Python

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