Hiring profile

Hands-on builder with CTO-level impact

I am Leonard Daume. Today I build product systems in which agents work from the problem through production, plus harnesses for developer teams. Behind that are my Cognitive Informatics studies, product sense, hands-on engineering, and operational delivery. That matters in contexts where product, technology, team, and operations have to work together: from 0→1 platforms to remotely scaling 100+ developers.

Software craft has been my path from the start. The origin is concrete: as a child, I was influenced by a first-generation Chaos Computer Club member who later became something close to a mentor around the C64, BASIC, text adventures, and early PCs. Then came computer science classes at Laborschule, first websites and a webshop in 1998/1999, and Java at Westfalenkolleg. That is why I still think about product, code, and operations as one system.

What this page makes visible

This page shows where I become productive early and how hands-on depth, product judgment, technical leadership, and CTO-level responsibility work together.

Talk about a role

Proof

CTO-level impact under scaling pressure

Shaped technical direction, team structure, and delivery systems for a scaling organization, including remotely scaling 100+ developers without adding another coordination layer.

Proof

AI foundation and daily practice

Cognitive Informatics studies spanning AI, ML, robotics, and multi-agent systems, plus productive daily LLM use since February 2023 and predominantly agent-based implementation since late 2025.

Proof

Hands-on through operations

Product, architecture, code, monorepos, queue orchestration, observability, IaC, and operational platform work stay connected in one profile.

Proof

Context breadth with a clear throughline

Repeated exposure to 0→1, rebuild, and scaling contexts. Useful when new environments need to be understood and made productive quickly.

Technical origin

First the working stance, then the professional proof.

This is about the origin: why I learned early to understand, build, and operate systems myself. The professional proof follows in the career context, timeline, and skills sections.

01

Early CCC influence

Technology entered my life as something you understand and build yourself, not as something you passively consume. That was shaped by a first-generation Chaos Computer Club member who later became something close to a mentor for computers, systems, and programming.

02

1992: BASIC and text adventures on a C64

With C64 BASIC, text adventures, and early PCs, early fascination turned into practical software work. That is where the thread of logic, system understanding, and building things myself begins.

03

Early 90s: Laborschule, DOS, Windows 3.11

Because of the university connection, computer science was available unusually early. That is where DOS, Windows 3.11, and the first more systematic programming exercises came in.

04

1998/1999 to 2002: web, webshop, Java

By the late 90s I was already building websites and running a webshop. In 2002, Java at Westfalenkolleg added a more formal software foundation. The builder thread has stayed intact ever since.

Hiring context

Many contexts, one clear line

If you are reviewing my CV, you will see several intense product and engineering contexts. The common thread is exposure to the whole system: 0→1 work, rebuilds, scaling, advisory, and hands-on delivery.

That matters for full-time hiring: I quickly understand how product logic, technical reality, team structure, and operations interact. That reduces ramp-up and increases the chance of durable impact over years.

For fractional or transformation work, the same range matters because unclear systems rarely have a single-point problem. I bring patterns that have worked in real teams without forcing every new context into an old template.

Experience

Experience that proves range

The timeline shows AI and ML judgment, delivery maturity, leadership effectiveness, and hands-on range.

09/2026 - 11/2026

Director Agentic AI

OH-SO Digital GmbH

Full-time

As Director Agentic AI, I work where client problems, product ideas, and technical reality meet. I carry technical responsibility across client projects, remain hands-on, and make recurring lessons useful to others.

07/2025 - 08/2026

Chief Technology Officer (Hands-on)

Livable Places GmbH

Full-time

Hands-on CTO work in an AI-native proptech startup: monorepo with Nuxt, Hono, Bun, BullMQ flows, and Redis locking, bare-metal platform with IaC and observability, plus product and architecture steering for geo/census services and an office-risk product line.

09/2023 - 06/2025

Chief Technology Officer

WPS MANAGEMENT GMBH // wescale

Full-time

As CTO, sharpened team structure, delivery systems, and technical direction for a scaling organization, including remotely scaling 100+ developers without adding another coordination layer.

06/2023 - 12/2023

Freelance Fullstack Developer & Consultant

LGE Energy Berlin GmbH // Zenstrom

Freelance

Combined consulting and implementation for a tailored website and portal solution instead of splitting concept and build into separate worlds.

09/2016 - 01/2018

Big data under real load

SISTRIX GmbH

Full-time

Handled even larger data volumes than before, with a focus on pipelines, scaling, and what it takes to keep systems calm under real load.

04/2016 - 08/2016

Java Team Lead & Project Manager

USU AG | Business Solutions

Full-time

Led the technical concept and implementation of a portal project across multiple participating companies and deliberately moved collaboration toward more agile ways of working through Scrum introduction, visible work, and iterative delivery.

05/2013 - 10/2013

Java EE development for a real estate portal

Immonet GmbH // Axel Springer AG

Full-time

Product and engineering work in a larger portal context with Java EE, agile development, and Scrum. Useful precisely because of the contrast to later startup and transformation work.

10/2010 - 02/2012

Learning infrastructure the hard way

Computacenter

Freelance

As a freelancer, I helped plan, build, and operate Telefonica Germany's data center. That was where it became obvious that software without operational reality is only half the job.

04/2009 - 11/2009

Early iPhone product work
Self-employed

Published a multi-touch game in the early App Store era. An early sign that I tend to work with new platforms instead of just watching them.

2008

First enterprise project

Synaxon AG

Co-developed a portal for correcting product descriptions in a university partnership. This was where learning turned into real team, product, and system work.

2008

Agile engineering starts to make sense

A master's course on process models showed me early that delivery is not a side effect of good engineering. It is a system you can design.

2002

Java on the second educational path

At Westfalenkolleg, I learned Java and moved my software foundation onto a much more systematic footing.

1998 - 1999

First websites and a webshop of my own
Self-employed

By the late 90s, I was already building websites and running a webshop. Not as a school exercise, but as an early sign that product, technology, and operations only make sense to me as one system.

1992

BASIC, text adventures, first programs of my own

On a C64, I wrote my first BASIC programs and text adventures. That early path was supported by a first-generation Chaos Computer Club member who became something close to a mentor for computers, systems, and programming.

Interactive route

Optional to play, without breaking the reading flow.

The route is an optional format for people who want to explore the career path interactively. The hiring signal still stays readable in the timeline, context, and skills sections without requiring interaction.

A small game instead of a static timeline.

Career Circuit

Collect each career checkpoint in sequence. The route only begins once you launch the run.

WASD / arrow keys or drag inside the stage
Mission

Mission

Start the run through every stop.

Collect each career checkpoint in sequence. The route only begins once you launch the run.

20 checkpointsWASD / arrow keys or drag inside the stage

Agentic foundation

Agentic product and software development with a technical foundation.

University of Bielefeld · Cognitive Informatics

My studies still shape my daily practice. Cognitive Informatics, machine learning, neural networks, and multi-agent systems still form the foundation for judging AI and ML in a systems context.

Today that becomes a clear working context: agents inside the product and across the full value stream, from triage and product clarification through development and verification to operations and learning. LLM workflows, agent skills, multi-agent research, and data and ML methods are mapped to real user value, model boundaries, and decision quality.

Core lens

Thinking about cognition, learning, perception, decision logic, and technical systems together.

Practical relevance

Helps treat agents as a product and systems question with explicit boundaries, sound context, and legible accountability.

Explore the agentic working model

Maturity before tool depth

AI, product, team, and delivery as one system

The technical deep dive follows after this. First comes the view of proven responsibility across product, engineering, leadership, and operations.

Proven responsibility

Product, team, and operations at a glance.

The signal is responsibility for product logic, technical direction, teams, delivery, operations, and the consequences of real decisions.

AI-native systems craft

AI treated as an agentic operating model: cognitive foundations, agent workflows, ML methods, artifact learning, and accountable delivery.

10.4y context years9 contexts
  • Cognitive science, ML, NLP
  • Agent skills, plugins, LLM workflows
  • AI-native SDLC, rules, artifacts

Product maturity

Product logic, prioritization, feedback loops, and 0→1 work treated together with architecture and delivery.

16.1y context years13 contexts
  • CTO & Chief Product Owner
  • Product model through operations
  • PMF, impact, and learning loops

Leadership maturity

Leadership as system work: direction, ownership, team building, coaching, and decisions that help teams carry responsibility.

11.4y context years7 contexts
  • 15 teams / 100+ developers
  • Hiring and team building
  • Coaching, review culture, ownership

Delivery and operational maturity

Delivery, shift-left quality, security, and operations as part of architecture: CI/CD, agile testing, DevSecOps, observability, SRE, and you-build-it-you-run-it.

18.3y context years15 contexts
  • CI/CD, IaC, and quality gates
  • Agile testing, DevSecOps, DORA
  • SRE, observability, operations

Ways of working and engineering culture

Delivery that holds up day to day

Agile practices are mapped to team ownership, short feedback loops, technical quality, and reliable product flow.

Ownership and flow

Teams get clear direction, visible work, small batches, and ownership for outcomes.

  • Agile Coaching
  • Agile Development
  • Kanban
  • Lean Development
  • Delivery ownership
  • Scrum
  • Team Collaboration

Quality and operations

Delivery maturity means built-in quality, agile testing, DevSecOps, CI/CD, reviews, and SRE are part of the system.

  • CI/CD
  • Code Review
  • DevSecOps
  • Documentation
  • Agile testing
  • SRE
  • Test-Driven Development

Product learning and systems thinking

Strategic direction, learning iterations, and ownership create delivery, product evolution, and innovation.

    Technical deep dive

    Core technical competencies

    The deep dive shows the system areas that matter now: AI/LLM, backend and data services, cloud scale, SQL/NoSQL, messaging, CI/CD pipelines, frontend, and quality. The signal is the combination of current practice, model judgment, and long-running production responsibility.

    Decision signals

    Current in practice, rarely combined, durable over time

    Relevant in 2026

    Active, market-relevant technologies for modern product and platform work.

    • Platform Operations & Observability

      15.7y in 13 contexts

      Active
    • NoSQL / Search / Graph

      15.0y in 10 contexts

      Active
    • Cloud Scaling & Microservices

      15.1y in 11 contexts

      Active
    • JavaScript

      16.4y in 13 contexts

      Active
    • SQL / Relational Databases

      15.8y in 11 contexts

      Active
    • CI/CD Pipelines

      16.4y in 12 contexts

      Active
    • TypeScript

      10.5y in 8 contexts

      Active
    • Python

      6.7y in 4 contexts

      Active

    Rare depth

    Niche production experience that differentiates quickly in hiring contexts.

    • Platform Operations & Observability

      15.7y in 13 contexts

      Active
    • NoSQL / Search / Graph

      15.0y in 10 contexts

      Active
    • Messaging & Event Streaming

      15.1y in 9 contexts

    • ArangoDB

      11.9y in 6 contexts

    • Docker Swarm

      9.8y in 5 contexts

    • Scala

      6.8y in 3 contexts

    Durable foundations

    Languages and platforms that also receive framework-derived experience.

    • Cloud Scaling & Microservices

      15.1y in 11 contexts

      Active
    • JavaScript

      16.4y in 13 contexts

      Active
    • SQL / Relational Databases

      15.8y in 11 contexts

      Active
    • CI/CD Pipelines

      16.4y in 12 contexts

      Active
    • TypeScript

      10.5y in 8 contexts

      Active
    • Python

      6.7y in 4 contexts

      Active
    • Java

      11.9y in 8 contexts

    • Machine Learning

      7.0y in 3 contexts

    Market-relevant core

    AI-native services, cloud scale, data, and delivery systems

    AI/LLM/ML

    AI/ML, LLM workflows, and agentic operating models connect method depth with product-adjacent delivery.

    LLM WorkflowsMCPClaude CodeCodex
    LLM Workflows
    Relevant in 2026
    Active nowsolid
    Overall years
    3.3y
    Contexts
    3
    Claude Code
    Relevant in 2026
    Active nowdeveloping
    Overall years
    1.6y
    Contexts
    2

    Backend & Data Services

    Languages, frameworks, APIs, microservices, and data-adjacent services carry scalable product logic.

    TypeScriptJavaPlay FrameworkAkka
    JavaScript
    Durable foundationsRelevant in 2026
    Active nowproven
    Overall years
    16.4y
    Contexts
    13
    TypeScript
    Durable foundationsRelevant in 2026
    Active nowproven
    Overall years
    10.5y
    Contexts
    8
    Python
    Durable foundationsRelevant in 2026
    Active nowproven
    Overall years
    6.7y
    Contexts
    4

    Cloud & Platform Engineering

    Cloud scale, microservices, platform operations, and observability show up as one connected system capability.

    AWSGCPDocker SwarmPlatform Operations & Observability
    Platform Operations & Observability
    Rare depthRelevant in 2026
    Active nowproven
    Overall years
    15.7y
    Contexts
    13
    Cloud Scaling & Microservices
    Durable foundationsRelevant in 2026
    Active nowproven
    Overall years
    15.1y
    Contexts
    11

    CI/CD & Delivery Pipelines

    Jenkins, GitHub Actions, GitLab CI, and Woodpecker are bundled into one delivery capability.

    CI/CD PipelinesJenkinsGitHub ActionsGitLab CI
    CI/CD Pipelines
    Durable foundationsRelevant in 2026
    Active nowproven
    Overall years
    16.4y
    Contexts
    12

    Databases & Storage

    Persistence breadth across SQL, relational databases, NoSQL, search, graph, cache, and storage.

    SQLCachingNoSQL / Search / GraphPostgreSQL
    NoSQL / Search / Graph
    Rare depthRelevant in 2026
    Active nowproven
    Overall years
    15.0y
    Contexts
    10
    SQL / Relational Databases
    Durable foundationsRelevant in 2026
    Active nowproven
    Overall years
    15.8y
    Contexts
    11

    Proven breadth

    Up to eight prioritized skills per system area

    Prioritized by proven duration, context breadth, and current relevance. Additional proven technologies remain discoverable in project evidence and search.

    AI/LLM/ML
    8
    • Machine Learning

      7.0y in 3 contexts
    • Natural Language Processing (NLP)

      7.0y in 3 contexts
    • LLM Workflows

      Active
      3.3y in 3 contexts
    • LLM-assisted Development

      Active
      4.9y in 7 contexts
    • MCP

      3.0y in 2 contexts
    • Agent Skills

      Active
      1.4y in 2 contexts
    • Claude Code

      Active
      1.6y in 2 contexts
    • Codex

      Active
      1.6y in 2 contexts
    Backend & Data Services
    8
    • Java

      11.9y in 8 contexts
    • TypeScript

      Active
      10.5y in 8 contexts
    • Python

      Active
      6.7y in 4 contexts
    • REST APIs

      16.0y in 11 contexts
    • Node.js

      9.4y in 5 contexts
    • Play Framework

      6.8y in 3 contexts
    • Akka

      6.8y in 3 contexts
    • Spring Boot

      5.9y in 3 contexts
    Data Engineering & Analytics
    8
    • Data Engineering

      5.0y in 3 contexts
    • Graph Data Modeling

      11.9y in 6 contexts
    • Conversion Analytics

      7.5y in 3 contexts
    • Big Data Processing

      3.8y in 2 contexts
    • GeoJSON

      4.1y in 2 contexts
    • Data Enrichment Pipelines

      2.4y in 1 context
    • Search Analytics

      2.4y in 1 context
    • XPath

      3.8y in 2 contexts
    Databases & Storage
    8
    • SQL

      Active
      15.8y in 11 contexts
    • Caching

      10.3y in 5 contexts
    • NoSQL / Search / Graph

      Active
      15.0y in 10 contexts
    • PostgreSQL

      Active
      7.3y in 5 contexts
    • ArangoDB

      11.9y in 6 contexts
    • MongoDB

      5.5y in 4 contexts
    • MySQL

      8.5y in 5 contexts
    • Database Systems

      Active
      15.8y in 11 contexts
    Messaging & Event Streaming
    8
    • Asynchronous Processing

      15.1y in 9 contexts
    • Message Queues & Pub/Sub

      12.2y in 8 contexts
    • Event-Driven Architecture

      12.2y in 8 contexts
    • AWS SQS

      3.4y in 1 context
    • Google Pub/Sub

      1.8y in 1 context
    • BullMQ

      1.2y in 1 context
    • JMS

      2.4y in 1 context
    • Webhooks

      2.9y in 1 context
    Cloud & Platform Engineering
    8
    • AWS

      10.6y in 6 contexts
    • GCP

      1.8y in 1 context
    • Docker

      Active
      10.7y in 8 contexts
    • Docker Swarm

      9.8y in 5 contexts
    • Kubernetes

      1.8y in 1 context
    • GKE

      1.8y in 1 context
    • Platform Operations & Observability

      Active
      15.7y in 13 contexts
    • Cloud Scaling & Microservices

      Active
      15.1y in 11 contexts
    CI/CD & Delivery Pipelines
    8
    • CI/CD Pipelines

      Active
      16.4y in 12 contexts
    • Jenkins

      5.5y in 3 contexts
    • GitHub Actions

      Active
      1.8y in 3 contexts
    • GitLab CI

      1.8y in 1 context
    • Drone CI

      3.4y in 1 context
    • CircleCI

      2.9y in 1 context
    • Woodpecker CI

      0.6y in 1 context
    • SonarQube

      1.8y in 1 context
    Frontend
    8
    • React

      Active
      7.3y in 5 contexts
    • Vue.js

      5.9y in 3 contexts
    • Next.js

      3.4y in 1 context
    • Tailwind CSS

      Active
      7.6y in 7 contexts
    • Astro

      0.6y in 1 context
    • PWA

      4.0y in 2 contexts
    • Nuxt

      Active
      1.4y in 2 contexts
    • CSS

      4.2y in 2 contexts
    Mobile & Native Apps
    8
    • iOS

      4.9y in 3 contexts
    • Android SDK

      3.4y in 1 context
    • Push Notifications

      4.3y in 2 contexts
    • Objective-C

      1.5y in 2 contexts
    • Apple Push Notifications

      4.3y in 2 contexts
    • Web Push Notifications

      3.4y in 1 context
    • Android Push Notifications

      3.4y in 1 context
    • Xcode

      0.8y in 1 context
    Agile Testing/Quality
    8
    • Test-Driven Development

      Active
      16.7y in 13 contexts
    • Test Automation & E2E Quality

      Active
      16.9y in 14 contexts
    • Cypress

      9.9y in 5 contexts
    • Playwright

      Active
      1.7y in 3 contexts
    • Vitest

      Active
      2.0y in 3 contexts
    • JUnit

      2.9y in 2 contexts
    • Mockito

      4.3y in 3 contexts
    • Selenium

      4.0y in 4 contexts
    Tooling
    8
    • Git

      14.3y in 9 contexts
    • Maven

      10.8y in 7 contexts
    • SBT

      6.8y in 3 contexts
    • Vite

      4.2y in 4 contexts
    • pnpm

      1.2y in 1 context
    • Turborepo

      1.2y in 1 context
    • n8n

      8.3y in 6 contexts
    • Zapier

      2.9y in 1 context

    Next step

    For a long-term role or collaboration, we can assess the context together directly.

    I reply personally with a clear view of the value I could add and the next useful step.