Welcome 👋

I'm Reinier Kruisbrink

DevOps- // AI- // Data- Engineer

It is not about perfection, it is about progress.

Who am I?

Curious // Resourceful // Open Minded // Goal Oriented

I am passionate about learning and exploring the patterns that underlie both life and nature. While my formal education lies in technology and informatics, I find inspiration in the fascinating domain of biology, from organic chemistry and botany to ecology. In my experience, there are striking similarities in the patterns that govern processes on all scales and in different fields from an abstract point of view. Unraveling these connections and combining this understanding with data enables the automation in technology and the intuition in life to make smarter, more efficient, and more robust choices. Additionally, my interests in linguistics and neuroscience add to my belief that, while language and thought are subjective, unraveling similarities and differences herein expose a richer and more open way of understanding and communicating. I am especially interested in applying this knowledge in bioinformatics and at the intersection of AI and life & health sciences. I firmly believe that the synergy and complementarity across different fields are rich sources of knowledge, pushing the boundaries of understanding.

From: Amsterdam ❌❌❌ The Netherlands 🇳🇱
Age: 27
Interests: AI | Biology | Linguistics | Neuroscience | Classics
Hobbies: Hockey | Football | Reading | Hiking | Skiing | Photography
Languages: 🇳🇱 🇬🇧 🇩🇪 🇳🇴 🇫🇷 🇪🇸

Education & Certifications

Bionformatics

UC San Diego [remote] (2024-Present)

Bioinformatics Specialization: This specialization addresses contemporary biological questions, ranging from DNA processing, genome sequencing to proteomics and molecular evolution, and ending with a big data project annotating the covid-19 genome.
Data Engineering & DevOps

IBM, Microsoft, Hashicorp (2023, 2024)

DevOps & Software Engineering: A track of mutliple courses on DevOps, Cloud Computing, Agile, Linux, Git, Docker & Kubernetes, CI/CD, and more.
Azure Data Engineering: Designing efficient (big) data processing pipelines using Azure services, implementing Data Warehouses and Lakehouses, leveraging SQL, Python, and Spark.
Hashicorp Cloud Engineer: General Infrastructure-as-Code (IaC) knowledge and cloud engineering as well as specifics for Hashicorp's Terraform.
BSc & MSc Artificial Intelligence

University of Amsterdam (2016-2020, 2020-2022)

I followed courses on the theoretical aspects (calculus, probability & information theory) and practical applications (model architectures and training) of machine and deep learning. My electives specialized in NLP and computational biology/neuroscience, leading to a MSc thesis exploring the intersection of these fields, focusing on information extraction from biomedical/chemical patents using NLP techniques. In my BSc thesis I explored hardware acceleration of neural networks on FPGAs.

Experience

AI & Data Engineering Consultant

Sia Partners (2023 - Present)

As a consultant I mostly do data engineering projects at our clients in sectors such as Energy, Finance, and Retail to prepare/maintain the infrastructure for data related applications such as AI models and reporting. Next to implementation I employ project delivery strategies to track, explain and validate progress with the client and/or other stakeholders.
Data Engineering Team Lead

Baise (2022)

I led a team of 5 students and was responsible for the division of and guidance on tasks within the project and the total streamlining of the development process. In this project we advised a datacenter security company to collect informative data and implement adequate infrastructure in the process of getting their data ready for models to forecast increased risk of security incidents.
Data Science Research Intern

Elsevier (2022)

Research and development of deep learning models to extract information on genes and proteins as drug targets from chemical patents and biomedical literature.

Skillsets

What can I do?

Data Science

I can analyze data and develop ML & DL models, with a theoretical background in probability & information theory, and practical knowledge of feature engineering and model choices.

MLOps

I can ensure seamless integration between machine learning models and operational processes with scheduling, containers, and model serving.

Data Engineering

I can implement effective and scalable data workflows by (big) data processing, transformation, and integration in various cloud infrastructures and database types.