Engineering leadership from the operator seat.
Petar Gusić
Head of Engineering at a European scale-up. I write about delivery systems, org design, and practical AI adoption in growing engineering teams.
About
Why teams slow down after scale
More headcount does not automatically create better delivery. In growing engineering organizations, management layers, planning overhead, and unclear ownership often create drag before anyone can name it clearly.
How engineering systems actually work
I'm interested in the operating mechanics behind team performance: org design, planning cadence, manager load, handoffs, priorities, and team topology.
Practical AI adoption
Over the last two years, I've worked on AI adoption in a real operating environment, not as a thought exercise. The interesting part is not tooling hype. It's how AI changes delivery, hiring, evaluation, and team expectations.
I've spent 20 years in software engineering, moving from hands-on technical work into engineering leadership and organizational design.
My path through tech has not been linear. It has included systems integration, frontend and product engineering, engineering management, and leadership across multiple squads in growing companies. That range matters because delivery problems rarely come from one layer of the system. They come from the interaction between priorities, structure, management load, team topology, and operating habits.
Today I lead engineering in a European scale-up. My work sits at the point where engineering execution, organizational design, and business reality meet.
What I care about most is the gap between how engineering organizations are supposed to work and how they actually work under pressure.
— why delivery gets slower after teams grow,
— why managers end up overloaded,
— why process gets heavier without creating clarity,
— and how AI changes expectations around hiring, evaluation, and team design.
Over the past two years, I've worked on AI adoption through multiple pilots in a real operating environment. The value of that experience is not that it makes for a better AI story. The value is that it forces practical decisions about delivery, quality, hiring, and leadership.
Advisory
I occasionally take on a small number of private advisory engagements through DeseiRose, focused on one offer: a fixed-fee review and diagnostic for engineering organizations that got slower as they scaled.
View DeseiRose →Experience
Career snapshot
20 years in software engineering and engineering leadership.
Currently leading 40+ engineers across multiple squads at a European scale-up. Past work spans hospitality tech, adtech, media analytics, edtech, and systems integration. My path has covered IC work, engineering management, org redesign, and operational leadership.
Contact
The easiest way to reach me is by email.
Expect a response within 2 business days. Based in Split, Croatia. Available across EU and US time zones.
For private advisory work, the commercial offer lives at DeseiRose.