Software Engineer
Product Engineer focused on building a self‑governing enterprise IT platform, leveraging Python, Machine Learning, and data engineering to model application landscapes at scale, with strong experience in AWS and SQL for data storage and analytics.
At Stackgini we are turning every business idea into technology impact.
In most large companies this journey is broken. A good idea lands in an IT organization that runs hundreds of applications, and nobody can tell you quickly what already exists, what is redundant or what to buy. So decisions happen in spreadsheets and gut feeling, and ideas wait.
We are changing that. Our platform understands the enterprise application landscape bottom-up, from individual demands to full portfolio interdependencies, and reasons about it the way the best human experts do, just at machine scale. Our vision: enterprise IT that is self-governing and self-optimizing. No longer a bottleneck, but the engine of innovation.
We are 9 people and we build this with Siemens Healthineers, REWE, Grünenthal and other leading enterprises. At this size there is no hiding. What you ship goes to customers fast, and the people who join now set the standard for the product, the culture and the category.
Tasks
The role
A Product Engineer at Stackgini owns features end to end. You take a customer problem, scope it with Product & CSM, design the solution, build it across the stack, ship it, and stay close to it after release.
You are close to users. You join customer calls, run user tests, read support context, and you use that input to decide what should exist and how it should behave. The person building the feature is expected to shape the feature.
We have more good ideas than hands. For most reasonable feature ideas the question is not whether we build them, but when. Product Engineers are the people who turn that prioritization into shipped product quickly, without lowering the bar.
The AI work
AI is core to our product, but we don't run agents for everything. We use them where they create real value. For that we built our own LLM harness from hard-earned production experience: maximum freedom for the model, without compromising on enterprise control, governance or EU data residency.
What this means for you: you work hands-on with these new technologies, you run evals instead of trusting demos, and you know how to put this stuff into production reliably. Agent output gets benchmarked against expert-curated baselines, and failure modes get named, measured and fixed. If you are obsessed with how AI systems behave, fail and improve, this is the place for you.
How we ship
Posted June 18, 2026