
QYRAGY GmbH
CTO & Chief Product Owner for AI Matching Platform
Chief Technology Officer & Chief Product Owner
Relevance
Why this case matters
This framing makes the decision signal explicit: impact, proof, fit, and AI / delivery relevance for hiring or collaboration.
System impact
Built an AI-powered matching platform from 0 to 1 and combined product, technology, and team design inside an early-stage startup.
AI / delivery relevance
Put AI into the product core early instead of treating it as a thin prompt layer on top.
Proof
0→1
platform built
AI matching
k-Means + NLP
PWA + AWS
scalable base
Especially relevant for
- Hiring
- Fractional
- Startup CTO
- AI-native systems craft
Case context
Overview
Smartjobr started as an AI-powered freelancer matching platform that had to move from first product model to real delivery. I connected matching logic, technical architecture, and team building in an early-stage startup with fast learning loops, product-market fit work, and direct delivery pressure.
The platform uses k-Means algorithm and NLP for matching between freelancers and projects. Next.js/PWA, AWS infrastructure, and automated BI dashboards connected product usage, technical foundation, and data-informed decisions.
Responsibility
Activities
- Startup CTO & CPO: Product strategy, technical direction, and team building across engineering, design, and marketing
- AI matching platform: k-Means algorithm, NLP, intelligent freelancer-project matching
- Next.js/PWA development: Cross-platform user experience, web & Android app
- AWS infrastructure: Lambda, S3, RDS, SQS, CloudFront for scalable architecture
- BI & automation: Dashboard development, Slack bot, automated KPI reporting
- Product strategy: Customer feedback, user experience, and learning loops
- Security & privacy: Data protection compliance, secure infrastructure monitoring
- Team leadership: Agile development, cross-functional collaboration, technology selection
Operating mode
Methodology
- Agile development: Scrum, Kanban, cross-functional teams
- Product management: Customer feedback, user experience, market validation
- Startup methodology: Rapid iteration, MVP-first, product-market fit
- AI/ML integration: Data-driven matching, algorithm optimization
- DevOps: AWS infrastructure, automated deployments, monitoring
Technical context
Technology stack
The tools are not the point by themselves. What matters is which system layers had to work together.
Frontend
5Backend
3Data & AI
3DevOps
5Mobile
2Tools
5Next step
If you want to explore similar leverage for hiring, collaboration, or a concrete transformation, this is the right starting point.
Send a short note about the situation you are trying to assess. I reply personally and will be direct about fit.