Kai Waehner
Global Field CTO At Confluent
Munich, Bavaria, Germany
Summary
🤩
Rockstar🎓
Top SchoolKai Waehner is Global Field CTO at Confluent with 15+ years of experience helping Fortune and Global 2000 customers modernize data architectures using event streaming, cloud-native patterns, and AI. He blends executive-facing strategic advisory with hands-on technical work—authoring books and articles, speaking globally, and contributing practical open-source examples that deploy ML models into production with Kafka Streams. Based in Munich, he has driven real-time solutions across industries from banking and automotive to manufacturing and healthcare, and often acts as the bridge between customer requirements and product roadmaps. Beyond public speaking, he routinely publishes deep-dive case studies (and a free book) and has directly modernized projects by updating Kafka Streams integrations and ML pipelines for production reliability.
14 years of coding experience
4 years of employment as a software developer
Diploma, Commercial Information Technology, Business Studies, Diploma, Commercial Information Technology, Business Studies at University of BambergHigher School Certificate, Science-oriented, Higher School Certificate, Science-oriented at Ehrenbürg-Gymnasium-Forchheim
Github Skills (9)
javas10
machine-learning10
kafka-streams10
java10
testing9
deeplearning-ai9
deep-learning9
h2o8
tensorflow8
Programming languages (10)
Github contributions (5)
kaiwaehner/kafka-streams-machine-learning-examples
Aug 2017 - Nov 2020
This project contains examples which demonstrate how to deploy analytic models to mission-critical, scalable production environments leveraging Apache Kafka and its Streams API. Models are built with Python, H2O, TensorFlow, Keras, DeepLearning4 and other technologies.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:78 commits, 5 PRs, 74 pushes in 3 years 3 months
Contributions summary:Kai primarily focused on updating and integrating the Kafka Streams API within the project. Their contributions included upgrading deprecated methods and constructors to the latest Kafka Streams API, along with modifying Kafka configuration properties. The user also worked on adapting the code to use the embedded Kafka cluster for testing and upgrading DL4J. Moreover, the user demonstrably worked on integrating Machine Learning models with the Kafka Streams setup.
pythonmission-criticalstreamskeras-tensorflowtensorflow
kaiwaehner/ksql-udf-deep-learning-mqtt-iot
Jul 2018 - Apr 2020
Deep Learning UDF for KSQL for Streaming Anomaly Detection of MQTT IoT Sensor Data
Contributions:40 commits, 1 PR, 28 pushes in 1 year 9 months
anomalyksqldeep-learninganomaly-detectionmqtt
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