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# AI Engineer

- **Company:** Alasco GmbH
- **Location:** München, Germany
- **Salary:** €90,000.0 - €120,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Information Engineering, Software Debugging, Python (Programming Language), Software Product Management, Rapid Prototyping Process, TypeScript, Unstructured Data, Large Language Models, Prompt Engineering, Code Restructuring
- **Published:** August 6, 2026
- **Apply:** https://www.xing.com/jobs/muenchen-ai-engineer-157049453

## About the Role

* Real product software experience: you've built software that actual users rely on, ideally with TypeScript and Python.
 * A deep understanding of LLM infrastructure: you've worked with agent architectures and systematic prompt design.
 * Expertise in context and data engineering: you know how to prepare unstructured data and use context windows efficiently, cutting noise and maximizing relevance.
 * Confidence in an existing codebase: you debug, refactor, and review your own code and other people's without hesitation.
 * A deliberate approach to AI coding tools: you use them regularly and can explain what you hand off to the tool and what you check yourself.
 * Fluent German and English: you communicate confidently in both languages day to Day.

## Description

We're turning Alasco into the leading agentic AI platform for real estate. Picture a project developer opening their dashboard and seeing at a glance whether their budget will hold in three months, because an autonomous agent in the background is running complex forecasts and flagging anomalies. You build that moment.
From the first idea to the metric that tells you whether it worked. You build the AI core features of our financial management product: orchestrating agent harness systems, writing evals that keep our models reliable in a finance-critical domain.
You own the full lifecycle: from rapid prototyping through context engineering to Observability.
What you'll do

 * Own agentic workflows end to end: you build autonomous systems that take on complex tasks.
 * Establish eval-driven development: you build evaluation frameworks and ground-truth sets that make agent reliability measurable and catch regressions before they reach production.
 * Work across our AI product areas: dashboards, reporting, valuations, and planning within financial management, depending on what the team is prioritizing.
 * Handle LLM ops and observability: you implement tracing (e.g. Langfuse) and monitoring to catch hallucinations and scale reliability.
 * Talk to customers: whenever a direct conversation leads to a better decision, whether with customers or internal stakeholders.
 * Name trade-offs directly: on time, cost, UX, or quality, you say it out loud instead of staying quiet.


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