AI and cloud platforms your team can run after handover.

Tech Lead mandate, architecture audit, and workshops for Kubernetes, CI/CD, Infrastructure as Code, and operating AI services. Augsburg and remote across DACH.

Free 30-minute intro call

No sales pitch. Reply within two business days.

SITUATIONS

Typical starting points

AI services are in use, but access, cost, and data outflow are unmanaged.
The container platform runs, yet operations take more time than before.
There is no one who owns the technical decisions.
The platform runs, but only two people on the team really understand it.

SERVICES

What I work with

DevOps, Platform & Operations

Your platform rolls out reproducibly and stays operable - with Kubernetes, CI/CD, IaC, and monitoring that your team owns.

AI Integration

AI services run against your systems with clear interfaces and access rules - including legacy via MCP and APIs.

Cloud Architecture

You get a target architecture and guardrails that hold under load, budget, and compliance.

ENGAGEMENT FORMATS

Three ways to work with me

Clear deliverables, knowledge transfer, no lock-in.

Tech Lead mandate

3-9 months

Cadence

2-4 days / week

Mode

Primarily remote

Right for you if ... Your platform is growing, but no one drives the technical decisions through.

Outcome: A running platform with documented decisions and a team that can carry it forward.

End-to-end architectural responsibility
Hands-on work and mentoring
Documentation, runbooks, and handover

Architecture Audit

2-4 weeks

Cadence

Focused

Mode

Remote + 1-2 on-site

Right for you if ... The platform runs, but risks, debt, and next steps are unclear.

Outcome: An audit report with a concrete roadmap for your platform - prioritized and actionable.

Stakeholder interviews and architecture review
Audit report with roadmap
Optional handover into engagement

Workshop

1-2 days

Cadence

Compact

Mode

On-site or remote

Right for you if ... Your team wants to connect AI assistants to your own systems and needs a workable access model.

Outcome: A running connection to one of your systems plus runbooks for the next steps.

Hands-on tied to your environment
Take-home artifacts and repos

Workshop details

PROJECTS

Selected references

On-premises Kubernetes platform

Context: Mid-sized industrial client, DACH. On-premises, about 30 environments.

Starting point: Containers ran individually on fixed hosts. Scaling and moving services between hosts meant manual intervention; every maintenance window was planned effort.

Role: Build of the Kubernetes platform and migration of existing workloads.

Outcome: Workloads distribute themselves across available nodes. Host failure or maintenance no longer requires manual intervention. Cluster setup is repeatable via idempotent Ansible roles, deployments use shared Helm templates, plus a monitoring stack with Prometheus and Grafana.

Loyalty platform across a store network

Context: Retail company, DACH. About 350 stores, project runtime six years, three of them in production.

Starting point: A loyalty program with app and card was to roll out across the entire store network. Relevant data sat across CRM, merchandise systems, and SAP. Point redemption had to work inside the checkout process, not afterwards, and every checkout in the network had to go through points calculation - even without a customer card.

Role: Technical project lead and DevOps engineer. Ownership of CI/CD pipeline build and operations, architecture decisions, and technical leadership of teams between two and twelve people.

Outcome: End-to-end CI/CD via Azure Pipelines and Docker, cloud operations on Azure, and a dedicated API for POS integration. Rollout started with a pilot store and covered the full network within three months. The platform processes about 220,000 checkouts per day on average, peaking at up to 600,000 during the Christmas season.

MCP server for AI agents

Context: In-house product, in production since 2026.

Starting point: AI agents need access to systems to be useful. A blank SSH or API-token grant is not a sound foundation for that.

Role: Concept, implementation, and operations.

Outcome: A modular MCP server in TypeScript with OIDC authentication, graded permission levels, and a discovery flow so an agent learns available operations per host instead of issuing arbitrary commands.

METHODOLOGY

Build for handover, not dependency

Structured onboarding

Interviews, inventory, and risk map as the starting point.

Documentation & pair-working

ADRs, runbooks, and hands-on in the team - no consultant black box.

Handover

Explicit transfer phase, optional standby afterwards.

ABOUT

About me

Hi, I'm Christian. I take technical ownership for AI and cloud platforms - from build-out through handover to your team.

More than twelve years of experience, including several years in technical project leadership and DevOps. I have led teams of two to twelve people.

Also: M.Sc. in Computer Science, a book chapter, and several conference talks.

Kubernetes, Azure, Terraform, TypeScript, LLM/MCP - stack-flexible
Working with CTOs, Heads of IT, and platform teams
Based in Augsburg, Germany - primarily remote across DACH

CONTACT

Book an initial call

Free 30-minute intro call

No sales pitch. Reply within two business days.

contact@c-ego.net LinkedIn XING