Machine Learning Developer
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.
ExperiencePython - 10 yearsAPIs - 4 yearsMachine Learning - 4 yearsTensorFlow - 4 yearsDjango - 3 yearsDevOps - 2 yearsChatbots - 2 years
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.
Senior Software Engineer
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.
Solutions Engineer (NLP)
- 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.
Freelance Data Scientist
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).
- 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.
Founder | Owner
- 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.
Student Application Prediction
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 Packhttps://github.com/RasaHQ/financial-demo
The implementation allowed the user to switch conversation contexts and receive guidance to succesfully complete conversations.
Rasa Advanced Deployment Workshophttps://www.udemy.com/course/rasa-advanced-deployment-workshop/
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
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 Applicationhttps://www.mscsoftware.com/assets/2221_SF303ZZZLTDAT.pdf
I was both a solver and a front-end developer.
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 Behaviorhttps://github.com/ArjaanBuijk/CarND_Behavioral_Cloning_P3/blob/master/README.md
Streamline Your Django Settings With Type Hints: A Pydantic Tutorial, Part 1
Optimize Your Environment for Development and Production: A Pydantic Tutorial, Part 2
How to Deploy Django on Heroku: A Pydantic Tutorial, Part 3
Security in Django Applications: A Pydantic Tutorial, Part 4
Django, Qt, Flask
NumPy, Pandas, Microsoft Foundation Class Library (MFC), Scikit-learn, TensorFlow, Matplotlib, Keras, Python Asyncio, Newman, XGBoost, PyQt, OpenCV
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
Docker, Jupyter Notebook, MacOS, Kubernetes, Ubuntu, Google Cloud Platform (GCP), Amazon Web Services (AWS), Heroku, Windows, Linux, Amazon EC2, OpenShift
Chatbots, Natural Language Processing (NLP), GitKraken, Machine Learning, CI/CD Pipelines, GPT, Generative Pre-trained Transformers (GPT), APIs, Sanic Web Server, GitHub Actions, GUI, FastAPI, Webhooks
DevOps, Agile, REST
Elasticsearch, Amazon S3 (AWS S3), Google Cloud, Data Pipelines, MongoDB, PostgreSQL
Master's Degree in Aerospace Engineering (CFD)
Delft University of Technology - Delft, the Netherlands
AWS Certified Cloud Practitioner
Amazon Web Services
Nanodegree, Cloud DevOps Engineer
Google Kubernetes Engine
Google via Coursera
Deeplearning.ai via Coursera
MongoDB for Developers
Nanodegree, Full-stack Web Development
Nanodegree, Self-driving Car Engineer
Retrieving, Processing, and Visualizing Data with Python