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

Verified Expert  in Engineering

Machine Learning Developer

Location
Plymouth, MI, United States
Toptal Member Since
June 4, 2018

Arjaan is a Python cloud developer and Rasa chatbot engineer with deep experience in web frameworks, APIs, machine learning, data science, and DevOps. He is also keen on several Python web frameworks like Django, Flask, and FastAPI and excels in a wide variety of Python libraries like Pandas, TensorFlow, and Rasa. Arjaan is a lifelong learner and seeks freelance clients to collaborate with on exciting and challenging projects.

Portfolio

US based SaaS company
Django, Postman, Newman, Docker Compose, Python, APIs, CircleCI, Bitbucket...
Rasa
TensorFlow, Ubuntu, Windows, DevOps, Pandas, GitHub, NumPy, Chatbots...
University of Colorado Boulder
Ubuntu, Windows, Keras, GitHub, NumPy, Python, Slack, Zoom, Jira, Bitbucket...

Experience

Availability

Part-time

Preferred Environment

Python, Django, Amazon Web Services (AWS)

The most amazing...

...recent creation is an API testing framework with Newman, CircleCI, and Bitbucket. It verifies REST and GraphQL APIs served by Django and Go.

Work Experience

Senior Software Engineer

2021 - PRESENT
US based SaaS company
  • Improved developer productivity by reducing local development set up from two days to one hour. Created a dockerized development environment for a SaaS application consisting of Django, Go, and React.
  • Created an API-testing framework using Postman and Newman. REST APIs are served by Django, and GraphQL APIs are served by Go. The tests run automatically as part of CI/CD workflows with CircleCI and Bitbucket.
  • Created detailed wiki pages in Confluence with instructions for using the dockerized development environment and the API-testing framework. Worked with the development team to implement the new tools and improve their workflows.
Technologies: Django, Postman, Newman, Docker Compose, Python, APIs, CircleCI, Bitbucket, Confluence, Jira, Agile

Solutions Engineer (NLP)

2019 - 2021
Rasa
  • 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, and 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 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.
  • Created and taught an online course on advanced deployment techniques with Kubernetes (https://www.udemy.com/course/rasa-advanced-deployment-workshop/).
  • 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 Smoke Tests using Python.
Technologies: TensorFlow, Ubuntu, Windows, DevOps, Pandas, GitHub, NumPy, Chatbots, Google Cloud Platform (GCP), Helm, Kubernetes, Docker, Python, Machine Learning, GPT, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Amazon Web Services (AWS), CI/CD Pipelines, GitHub Actions, Rasa.ai, CircleCI, Python Asyncio, PostgreSQL, Webhooks, APIs

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, Amazon S3 (AWS S3), Jupyter, Scikit-learn, XGBoost, TensorFlow, Machine Learning, Amazon Web Services (AWS), APIs

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 (MFC) Library, Microsoft Foundation Classes (MFC), Graphical User Interface (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

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.

Financial Chatbot Starter Pack

https://github.com/RasaHQ/financial-demo
I designed and implemented NLU data, dialog stories, forms, and custom actions (Python) for a Rasa financial chatbot starter-pack.

The implementation allowed the user to switch conversation contexts and receive guidance to succesfully complete conversations.

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

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

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.

Flask Catalog

This portfolio project demonstrates a Flask-based implementation of a secure user model with registration, email confirmation, reset, and login, combined with an example of a catalog of items grouped into 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.

Use of Deep Learning to Clone Driving Behavior

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.
1982 - 1988

Master's Degree in Aerospace Engineering (CFD)

Delft University of Technology - Delft, the Netherlands

AUGUST 2021 - AUGUST 2024

AWS Certified Cloud Practitioner

Amazon Web Services

MARCH 2021 - PRESENT

Nanodegree, Cloud DevOps Engineer

Udacity

JANUARY 2020 - PRESENT

Google Kubernetes Engine

Google via Coursera

JANUARY 2019 - PRESENT

Deep Learning

Deeplearning.ai via Coursera

SEPTEMBER 2018 - PRESENT

MongoDB for Developers

MongoDB University

MAY 2018 - PRESENT

Nanodegree, Full-stack Web Development

Udacity

DECEMBER 2017 - PRESENT

Nanodegree, Self-driving Car Engineer

Udacity

JANUARY 2016 - PRESENT

Retrieving, Processing, and Visualizing Data with Python

Coursera

Libraries/APIs

NumPy, Pandas, Microsoft Foundation Class (MFC) Library, Scikit-learn, TensorFlow, Matplotlib, Keras, Python Asyncio, Newman, Microsoft Foundation Classes (MFC), XGBoost, PyQt, OpenCV

Tools

Rasa.ai, GitHub, Wingware IDE, Docker Compose, Google Kubernetes Engine (GKE), Helm, GitLab, Amazon EKS, AWS CloudFormation, Amazon Virtual Private Cloud (VPC), Postman, Confluence, Jira, Jupyter, Bitbucket, Zoom, Slack, AWS Glue, CircleCI, Ansible

Frameworks

Django, Qt, Flask

Languages

Python, C++, SQL, Fortran, JavaScript

Platforms

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

Storage

Elasticsearch, Amazon S3 (AWS S3), Google Cloud, Data Pipelines, MongoDB, PostgreSQL

Paradigms

DevOps, Agile, REST

Other

Chatbots, Natural Language Processing (NLP), GitKraken, Machine Learning, CI/CD Pipelines, GPT, Generative Pre-trained Transformers (GPT), APIs, Sanic Web Server, GitHub Actions, Graphical User Interface (GUI), FastAPI, Webhooks

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