KM - Engineering Leadership

Engineering Leadership

Profile

20+ years in software, most of them leading engineering teams, several on the leadership side. My focus has always been the same: get the technical foundations and workflows right, and keep good engineers engaged on problems that matter. AI is fundamentally changing how software gets built, and not just at the code level. It's reshaping how teams are structured, how work is planned, how quality is ensured, how delivery flows... I work that problem on two levels at once: alongside the leadership team on operating model and structure, and close to engineers facing the day-to-day realities.

"Development silos are a thing of the past. The future needs collaborative working models, human-to-agent, human-to-human, and agent-to-agent."

Engineering Leadership Agentic AI Human-AI Workflow Design SDLC Transformation Team Building

Problem. Solution. Why Me.

Problem

AI is in the tools. Not in the workflow.

Most engineering teams have copilots and chat tools. Almost none have redesigned how work is structured, planned, or delivered around them. The productivity gains stay at the individual level. Delivery doesn't change. The gap between L1 tool adoption and L3 orchestrated delivery is where most organisations are stuck, and most leaders don't know how to cross it.

Solution

Design the pipeline to feed agents context by design.

Agents don't need better prompts. They need requirements, epics, stories, and architecture specs structured so the pipeline itself provides context. That's an SDLC redesign, not a tooling purchase. It needs someone who understands both the leadership operating model and the daily engineering reality.

Why Me

I've been on both sides of the table for 20+ years.

I've sat on leadership teams and I've shipped production code. I co-founded a healthtech start-up and I've run multi-team engineering at a telco. I'm practising agentic development now, not just reading about it. And I've learned that transformation fails when the engineer doesn't trust it. Getting that trust is a skill. I have it.

The playbook for AI-era software delivery doesn't exist yet. That's the problem I want to work on next.

Background

20+ Years in Software

From hands-on engineering to leadership team, from Symbian C++ to iOS/Android to agentic dev. The thread throughout has been the same: build the foundations, earn the trust of engineers, and ship things that matter.

↗ NEXT

Agentic SDLC Leadership

Next role

The playbook for AI-era software delivery isn't written yet. That's the problem I want to work on next: designing the operating model, the workflows, and the governance that let teams actually operate in a human-agent world.

2023 — Oct 2026

Head of Application Development / Senior Scrum Master

Sunrise Communications AG

Engineering manager for multiple frontend teams (TV / streaming), technology and methodology decisions, Agile practice refinement, AI tooling adoption (Claude Code, Gemini, Copilot). Leadership team member, holding the line between strategic intent and engineering reality.

2021 — 2023

Technical Lead, Big Screen Team

Sunrise Communications AG

Native Android TV and Apple TV delivery, new frontend architecture, CI/CD pipeline buildout.

2017 — 2021

Co-founder, Principal Engineer, then Head of Product

Komed Health AG

Co-founded a healthtech start-up (secure clinical communication, "Slack for Hospitals"). Built the product from the ground up as Principal Engineer, then stepped into Head of Product: dev team management, roadmap, hiring, budget responsibility.

2001 — 2017

Earlier Career

Nokia, SwissSign, Digia, Ericsson

Mobile platforms from Symbian Qt C++ to iOS. Progressed from junior developer to technical lead of teams up to 8 people. Deep hands-on engineering across telecom, security and identity.

Engineering Leadership

Technical Credibility

20+ years hands-on, most of them in production environments. Currently using Claude Code and Gemini for agentic development. Can review architecture decisions, join technical debates, and know when something is actually hard versus just unfamiliar.

Leadership Team Fluency

8+ years on leadership teams at Sunrise and Komed Health. Can translate between engineering reality and strategic intent without watering either down. Stakeholder management without losing the engineers.

Team Building

Built and led teams up to 15 developers. Raised test coverage from zero to ~75% on each focus platform. Led the shift to cross-functional teams at Sunrise. The metric that matters: did engineers grow, and did they stay?

Agile and SDLC

Scrum Master and a long hands-on practice. But the point isn't the framework, it's delivery cadence, value, shared ownership, and a backlog that reflects what's actually shippable, and workflows that actually deliver that value.

Focus

What I'm Working On

Practising what I preach - concepts to proven workflows.

Agentic SDLC Playbook

Designing the end-to-end model for software delivery where agents operate inside the pipeline. From requirements to verification, with human oversight embedded by design rather than bolted on.

Human-Agent Collaboration Patterns

Three patterns matter: human-to-agent (delegation with oversight), human-to-human (the collaboration model changes when both parties use agents), and agent-to-agent (orchestration with defined trust boundaries).

Team Transformation

Hands-on AI tooling adoption with developer teams, building trust and genuine capability rather than compliance.

Hands-on

Tooling. md files. Agents. Skills. handovers. AI workflows. Human workflows. Quality gates. Actual products delivered.

Based in Switzerland, Zürich region.

Approach

How I Work the Problem

Engineering leadership in the AI era isn't a technical problem. It's an organisational, cultural, and workflow problem that happens to involve a lot of technology.

Foundations First

Test coverage, CI/CD, clear ownership, meaningful backlogs. These aren't glamorous, but organisations that skip them can't absorb AI tooling effectively. Every serious transformation I've led started here.

Two Levels at Once

Being credible with the leadership team on operating model decisions and staying close enough to engineers to know what's actually happening. I operate at both levels at once, and that's what makes transformation stick.

Engineers as the Constraint

Good engineers are scarce and easily disengaged. Tools don't fix that. Culture, autonomy, and meaningful work do. AI adoption fails when it feels imposed. It works when engineers feel like they're gaining capability, not being replaced.

Context by Design, Not by Prompt

Agents don't receive context from runtime prompts. They receive it from the SDLC pipeline: requirements, epics, user stories, architecture specs. The organisation that structures its pipeline to feed agents correctly is the one that gets reliable output. That redesign is a leadership problem, not a tooling one.

Maturity Model

Where Teams Are Today

Most organisations sit at L1 or L2, ad-hoc AI tool adoption with no systemic redesign. The transformation doesn't happen automatically, it requires deliberate leadership at the critical threshold and beyond.

The gap between L2 and L3 is where most transformations stall. Crossing it requires redesigning workflows, governance, and team structure, not just buying more tools.

Org Model

From Silos to Onion-Layer Org

The silo model was designed for a world where handoffs were unavoidable. In an agentic pipeline, the context loss at every handoff is what breaks delivery. The onion model routes context inward from strategy through to execution, with humans embedded at each layer boundary.

Context flows inward through the pipeline, not from runtime prompts. The org is structured to optimise delivery, not the other way around.