DevOps, Platform & Operations
Kubernetes, CI/CD, IaC, and monitoring – reproducible rollouts instead of constant firefighting.
Hi, I'm Christian. I take technical ownership in platform, AI, and cloud projects. My goal? To bridge thoughtful target architecture and stress-free, stable operations – together with strong teams.
Cloud-native architecture · Kubernetes · DevOps · AI integration
Kubernetes, CI/CD, IaC, and monitoring – reproducible rollouts instead of constant firefighting.
LLMs and agents where interfaces and operations are sound – including legacy via MCP and APIs.
Target architectures and guardrails that hold under real constraints and compliance.
Clear deliverables, knowledge transfer, no lock-in.
Mid-sized industrial client, DACH
Context: Greenfield setup of an on-premises Kubernetes platform for multiple containerized services – in an industrial company without existing platform expertise on the team.
Role: Architecture and implementation of cluster, ingress, storage, deployment standards, and monitoring. Structured knowledge transfer to the technical contact in parallel.
Outcome: Reproducible infrastructure with Ansible and Helm, significantly less operational overhead. The internal contact runs the platform independently today.
Retail, multiple sub-project teams
Context: Greenfield development of a customer-facing loyalty and bonus platform (app, backend, couponing, integrations) for end customers – set within mature corporate structures and legacy IT, coordinated across multiple sub-project teams.
Role: Technical project leadership with responsibility for architecture decisions, guardrails, and coordination across sub-project boundaries – including integration with existing and SAP-adjacent systems.
Outcome: Clearer technical guardrails, more stable integration flows into a mature system landscape, and faster delivery of new features despite distributed responsibilities.
Telecommunications, conference showcase
Context: Innovation project to integrate AI assistants into an existing telephony infrastructure in a production-adjacent way – presented as a tech teaser at an industry conference.
Role: Target architecture, definition of interfaces between AI and telephony, iterative prototype implementation.
Outcome: A working prototype with a clear statement on how LLM capabilities can be meaningfully connected to production-adjacent systems – without ignoring architecture and operations.
2026 – present
Christian Ego – Cloud, DevOps & AI Consulting
2018 – 2026
2016 – 2018
2014 – 2016
2008 – 2014
Augsburg University of Applied Sciences
During studies
Interviews, inventory, and risk map as the starting point.
ADRs, runbooks, and hands-on in the team – no consultant black box.
Explicit transfer phase, optional standby afterwards.
One point of contact from architecture through delivery. Most projects start with a short intro call.