Arjaan Buijk

I worked as a platform engineer building and maintaining AWS infrastructure for a healthcare technology platform. My responsibilities included designing Terraform modules for multi-environment deployments (development, staging, and production), developing Python Lambda functions for operational alerting, implementing secure secrets management with AWS Secrets Manager, and building CI/CD pipelines with GitHub Actions.

Key focus areas: Infrastructure-as-code, observability systems, and security-first cloud architecture.

funnAI is the next-generation AI ecosystem and first-ever implementation of the Proof-of-AI-Work (PoAIW) Protocol—a new consensus model where AI models compete, validate, and earn rewards in a fully decentralized environment.

icpp-pro is a comprehensive C++ development toolkit for the Internet Computer, designed to support the full on-chain execution of llama.cpp large language models as the core engine for AI agents.

It has 65,000 downloads from PyPI.

llama_cpp_canister is a fully on-chain large language model (LLM) smart contract for the Internet Computer. It compiles llama.cpp into WebAssembly, allowing quantized GGUF models (such as Qwen and DeepSeek) to run entirely on-chain without relying on off-chain compute. I developed the C++ core, built Python tools for model upload and caching, and implemented a complete CI/CD pipeline for WebAssembly builds, model compression, and automated testing.

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.

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 users' questions and finds the most similar results by querying Elasticsearch.

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.