
WPS MANAGEMENT GMBH
CTO at WPS MANAGEMENT // wescale
Chief Technology Officer
Relevance
Why this case matters
The case shows how problem understanding, responsibility, decisions, and implementation came together in this context.
Impact
Strengthened a delivery system across 15 teams, scaled 100+ developers remotely, and replaced activity metrics with flow-based execution.
What carries forward today
Established delivery rules, golden paths, and guardrails for a large-scale engineering system under real operational pressure; in parallel, early and continuous LLM use plus my own role-based agent workflows in n8n, complemented by experiments with locally hosted models.
Defensible proof
15 teams
led cross-functionally
100+ devs
scaled remotely
Flow
shorter, more reliable deployment cycles
Where this experience creates value
- Hiring
- Head of Engineering
- CTO-level
- Fractional transformation
Case context
Overview
WPS MANAGEMENT needed remote product development at 100+ developer scale to stay steerable without adding another coordination layer. I led 15 cross-functional Scrum teams with FaST Agile, flow signals (cycle time, lead time), and cloud-native architecture on GCP/Azure in a you build it, you run it model. I also covered interim Product Owner responsibility for one team. The setup included internationally distributed freelancers, including Turkey and Cyprus, plus technical alignment with team leads around GKE-adjacent platform responsibility.
The work shifted attention from output steering to better delivery frequency, higher team autonomy, and clearer golden paths. The important part is not the framework. It is that product development can stay steerable even in large remote scaling.
In parallel, I brought AI into daily work early: continuous productive LLM use since February 2023 (starting with GPT-3.5) plus my own role-based assistant and agent workflows in n8n (Assistant node with memory) for recurring engineering, analysis, and delivery tasks. On top of that came MCP connections and Microsoft 365 Copilot (at its core an OpenAI model with Microsoft Graph grounding) in the work environment, plus a few small tests with GitHub Copilot. I also experimented with locally hosted models to weigh privacy, cost, and control trade-offs in practice. This was not tool hype but concrete workload relief with clear boundaries.
Responsibility
Activities
- Remote leadership of 15 Scrum teams with 100+ developers without adding another coordination layer
- Led the Agile Coaches team and evolved product ownership, flow, and delivery practices
- Covered interim Product Owner responsibility for one team from problem definition and prioritization through delivery and operations
- FaST Agile implementation: flow signals, team autonomy, golden paths
- Cloud-native transformation (GCP/Azure): Kubernetes, CI/CD, infrastructure-as-code
- GitLab/GitLab CI and GKE context in the platform and delivery landscape; operational GKE responsibility sat with the responsible team lead
- Microservices architecture: Spring Boot, Docker, Kubernetes, service mesh
- DevOps culture: "You build it, you run it", Cypress tests, automated testing, monitoring
- Compliance governance: first PCI DSS 4.0 draft and recurring alignment with Visa stakeholders
- Distributed organization: integrated internationally distributed freelancers and external capacity into shared quality, review, and delivery standards
- Product thinking: Outcome-focused development, user value measurement
- Team enablement: shared ownership and cross-functional collaboration
- AI in my own daily work (personal productivity and evaluation, not a team-wide rollout): built my own role-based assistant and agent workflows in n8n (Assistant node with memory) and used LLMs productively every day from February 2023 for my engineering, analysis, and delivery tasks; complemented by MCP connections, Microsoft 365 Copilot, small GitHub Copilot tests, and experiments with locally hosted models to assess privacy, cost, and control
Operating mode
Methodology
- FaST Agile: flow signals, team autonomy, outcome focus
- Scrum: Cross-functional teams, sprint planning, retrospectives
- DevOps: Continuous integration/deployment, Cypress tests, infrastructure-as-code
- Product thinking: User value, impact measurement, iterative development
- Team enablement: shared ownership and golden paths
- AI as a tool: LLM and agent workflows in my own work where they measurably speed up delivery and analysis, with deliberate attention to privacy, cost, and control (including locally hosted models)
Technical context
Technology stack
The tools are not the point by themselves. What matters is which system layers had to work together.
DevOps
12Databases & Storage
4Messaging & Event Streaming
1Frontend
4Tools
9Practices
7Backend
8CI/CD & Delivery Pipelines
3Data & AI
4Next step
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