Richard Deetlefs

AI Innovator & Practical Engineer

I am passionate about building valuable products.

John Doe

Professional Experience

Founder

headwAI GmbH | May 2023 - Present | Vienna, Austria

In founder mode.

Chief Artificial Intelligence Officer (CAIO)

Sensonic GmbH | Aug 2023 - Mar 2024 | Vienna, Austria

As CAIO I was responsible for architecting long-term AI strategies and bridging technical expertise with business goals. I lead AI project management to drive innovation and collaboration.

Technical Lead of Artificial Intelligence

Sensonic GmbH | Aug 2021 - Aug 2023 | Vienna, Austria

Led interdisciplinary teams in the research and development of data-driven products, focusing on integrating advanced AI technologies into practical applications.

Product Owner Continuous Track Monitoring

Sensonic GmbH | Aug 2020 - Dec 2021 | Sankt Marienkirchen bei Schärding, Upper Austria, Austria

Oversaw the entire product life cycle, specializing in asset condition monitoring and machine learning. As a domain expert in asset condition monitoring and machine learning, I coupled my passion from both fields in order to build intelligent condition monitoring solutions for the railway industry.

Data Scientist

Sensonic GmbH | Apr 2019 - Sep 2020 | Sankt Marienkirchen bei Schärding, Upper Austria, Austria

Researched AI technologies to detect, classify, and predict the remaining useful life of defects on railway lines using distributed fibre sensing.

Postgraduate Researcher

University of Pretoria | Jan 2018 - Jan 2020 | Pretoria, South Africa

My research was focused on the application of machine learning, deep learning and computer vision for condition monitoring of mechanical systems.

Education

Master of Engineering (MEng) in Machine Learning, Deep Learning and Computer Vision in Mechanical Engineering

University of Pretoria | 2018 - 2018

Graduated with distinction.

In my master's dissertation, I solely developed a machine learning product to detect anomalies/defects on the surface of railway tracks. I collected my own data and trained a deep generative model to learn the class of healthy images. I then used post-processing techniques to perform unsupervised segmentation on anomalous areas. My algorithm can detect defects in real-time (exceeding the speed of the world's fastest train) using high-resolution images. Additionally, I have used machine learning and deep learning approaches to monitor rotating assets (bearing, gears, etc.) through sensor information (accelerators, temperature, etc.).

Bachelor of Engineering Honours (BEngHons) in Machine Learning in Mechanical Engineering

University of Pretoria | 2017 - 2017

Graduated with distinction.

Bachelor of Engineering (BEng) in Mechanical Engineering

University of Pretoria | 2013 - 2016

Graduated with distinction.