Thomas Pfeiffer
Managing AI Solution Architect - Automotive Industry at Capgemini
Frankfurt, Hesse, Germany
Summary
👤
Senior🎓
Top SchoolThomas Pfeiffer is a Managing AI Solution Architect focused on the automotive industry with nine years of hands-on experience designing and delivering scalable AI and cloud-native software solutions. He progressed through technical and advisory AI architect roles at IBM before joining Capgemini, combining deep software engineering fundamentals with MLOps, automation, and enterprise project leadership. Thomas excels at turning complex ML prototypes into production-grade systems using repeatable design patterns and cloud automation, often coordinating cross-functional teams across DACH and global consulting centers. Based in Frankfurt, he brings a pragmatic balance of technical depth and client-facing delivery, and his background in business informatics gives him a strong appreciation for aligning AI initiatives with measurable business outcomes.
9 years of coding experience
10 years of employment as a software developer
Bachelor of Science (B.Sc.), Bachelor of Science (B.Sc.) at Duale Hochschule Baden-Württemberg
German, English, German
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (15)
versioning10
analytics9
data-science9
devops9
python9
ai9
machine-learning9
mlops9
monitoring9
ibm-watson7
watson7
apache5
text-to-speech4
speech-to-text4
apache-iceberg2
Programming languages (4)
Github contributions (5)
IBM/data-science-best-practices
May 2021 - Mar 2022
The goal of this repository is to enable data scientists and ML engineers to develop data science use cases and making it ready for production use. This means focusing on the versioning, scalability, monitoring and engineering of the solution.
Contributions:1 review, 23 commits, 5 PRs in 10 months
data-sciencemachine-learningmonitoringversioningai
thomas-pfeiffer/data-science-best-practices
Nov 2021 - Nov 2021
The goal of this repository is to enable data scientists and ML engineers to develop data science use cases and making it ready for production use. This means focusing on the versioning, scalability, monitoring and engineering of the solution.
Contributions:4 pushes in 1 day
scientistscasespythongoalengineering
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