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Fractional AI Leadership for ML startups and tech companies
I work with founders and investors who are building AI-driven products and need senior AI leadership in a flexible allocation. After many years working hands-on and later leading applied machine learning teams in large-scale production environments, I now support ML startups and tech companies as a fractional AI leader. My role sits between strategy and execution: shaping AI direction, translating product goals into robust ML systems, and helping teams move from experimentation to reliable production. Alongside the technical work, I focus strongly on team leadership and capability building. This includes coaching ML engineers, setting clear technical standards, and supporting founders in building and scaling effective AI teams. I also work with leadership teams on questions around organization design, hiring priorities, and long-term technical direction. Engagements range from hands-on leadership to advisory support for founders and venture capital investors, typically at critical stages such as early architecture decisions, applied ML, production readiness, and team scaling. Visit www.triefenbach.ai for more details.
University of Ghent
Doctor of Engineering
January 1, 2009 – January 1, 2014
University of Applied Sciences Mannheim
Master of Science
January 1, 2008 – January 1, 2009
University of Applied Sciences South Westphalia
Bachelor of Engineering
January 1, 2005 – January 1, 2008
Self-employed
Fractional AI Leadership
February 1, 2026 – Present
Germany
Amazon
Applied Scientist → Senior Manager, Applied Science | Alexa & Nova
January 1, 2014 – January 1, 2025
Aachen · Hybrid
Cultural Fit Analysis
The candidate's career progression from an individual contributor to senior leadership at a large tech company like Amazon, followed by fractional AI leadership for startups, demonstrates adaptability and a broad range of experience. This suggests a potential fit for roles requiring both corporate rigor and entrepreneurial agility. The focus on AI strategy and applied machine learning aligns well with organizations seeking advanced AI capabilities. However, the lack of diverse project experience outside of AI/ML might limit fit for roles requiring broader software engineering expertise.
Soft Skills & Operational Fit
The candidate's experience as a Senior Manager and Fractional AI Leadership suggests strong leadership, strategic thinking, and team development skills. The description of owning the end-to-end ML lifecycle implies strong project management and operational capabilities. However, without psychometric test results, specific soft skill assessments like stress handling or team collaboration cannot be objectively evaluated.