100
Complex projects delivered
Product Engineering
I help teams own product outcomes end-to-end, from problem framing to shipped value.
I replace handoff chains with one accountable operator model, aligning product decisions, engineering execution, and business impact across SMEs, high-paced startups, and enterprise innovation teams.
SME operators High-paced startup teams Enterprise innovation and product departments
15+
Years of experience in Enterprise/Startup
15
AI-first projects completed
Trusted By
Powering Innovation for
Enterprise Leaders
Automotive · Telecommunications · Fintech · Digital Products
Problem
Most teams are not blocked by ideas. They are blocked by ownership gaps.
The same pattern repeats: insights exist, demos exist, interest exists, but outcomes do not scale because discovery, execution, and measurement are fragmented.
Too many ideas, no decision system
Roadmaps are driven by urgency and internal opinions instead of validated opportunities and constraints.
Cost: Teams spend quarters on low-impact bets.
Work ships, outcomes stay unclear
Features launch without a shared model for activation, retention, reliability, and commercial value.
Cost: Leadership questions budget and direction.
Cross-functional drag kills momentum
Product, data, and engineering move at different speeds with no single owner for end-to-end delivery.
Cost: Execution stalls at handoff boundaries.
Services
What Product Engineering means, and how I engage.
A Product Engineer owns outcomes, not just tickets. I can support one step of the process or run all four steps end-to-end with the team.
AI Strategy Sprint
Stop guessing what to build next.
Result A ranked execution plan with explicit tradeoffs, ownership, and the next 90 days de-risked.
- Opportunity map across user pain, value, and complexity
- Feasibility screen across architecture, data, and dependencies
- Prioritization scorecard with decision rationale
- 90-day product engineering roadmap with owners
Proof of Concept
You need to know if it works before committing to production.
Result A working prototype and a clear path to production.
- Rapid prototypes using open-source and commercial models
- Model and pipeline experimentation report
- First working AI workflows with performance and cost benchmarks
- Production-readiness assessment and recommended next steps
AI Product Lead
You have momentum, but no single owner for outcomes.
Result Faster releases, tighter execution rhythm, and clear accountability from discovery through launch.
- Weekly operating cadence across product, data, and engineering
- Milestone plan tied to measurable outcomes
- Risk and dependency management with clear owners
- Launch readiness checklist and release orchestration
AI Value & Capability Transfer
Shipping is not enough if the impact is invisible.
Result A repeatable value system that ties product metrics to business decisions and scale-up confidence.
- KPI tree linking usage and quality metrics to business outcomes
- Outcome review cadence with clear owners and definitions
- Executive-ready reporting templates for budget conversations
- Capability transfer plan for internal product engineering leadership
ICP Fit
Where this model works best
Simple qualification for operators who need one person to connect product decisions, engineering execution, and measurable outcomes.
SME Operators
Lean teams where one person needs to connect roadmap choices, implementation, and business priorities.
Expected outcome
Faster shipping with fewer false starts and clearer decisions about where to invest next.
Typical trigger
I am most useful when delivery is being juggled without a dedicated product leadership layer.
High-Paced Startups
Teams scaling quickly that need to keep product velocity high without sacrificing quality or focus.
Expected outcome
Higher experiment throughput, tighter execution cycles, and clearer signal on what drives growth.
Typical trigger
I am most useful when backlog volume is high but impact per sprint is inconsistent.
Enterprise Innovation/Product Teams
Departments under pressure to turn pilots into production outcomes with governance and executive clarity.
Expected outcome
Production-grade delivery with transparent value reporting and stronger stakeholder confidence.
Typical trigger
I am most useful when demo progress exists but outcomes are not yet defensible at executive level.
Community
Part of Munich's MLOps Scene
Co-organizer of one of Europe's largest MLOps communities. 40K+ practitioners.
Seats Filling Fast
Munich MLOps Meetup
Jan 28, 2026
Gen AI at Scale: from experimentation bottlenecks to production agents.
4 spots left RSVP
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Reach thousands of ML practitioners and engineering leaders across the region.
Contact
Need outcome ownership across product and engineering?
Book a discovery call and I will define the highest-leverage initiative, delivery model, and success metrics.
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