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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** Dedalus HealthCare GmbH - **Location:** Austria - **Salary:** €45,800.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Automation of Tests, Data Governance, Python (Programming Language), Software Engineering, Cloud Platform System, Large Language Models, Caching, Containerization, Information Technology, Api Design, Software Version Control - **Published:** August 28, 2026 - **Apply:** https://itjobsaustria.at/u/jobs/submit-application/12624/AI-Engineer-m-f-d-Healthcare-IT?contact_mail=healthcare.de%40dedalus.com&job_title=AI+Engineer+(m%2Ff%2Fd)+-+Healthcare+IT ## About the Role * Demonstrated, shipped production AI systems (preferably LLM/agent systems) -- real systems that ran in front of real users, not notebooks or proofs-of-concept. Be ready to walk us through one, including how it failed and what you did about it. * Strong software engineering fundamentals: Python, API design, automated testing, version control, containerisation. * Hands-on agent orchestration with at least one framework and a clear understanding of how agentic systems break in production. * Practical RAG experience: embeddings and vector stores, retrieval design, and retrieval-specific evaluation. * LLM evaluation experience: building or operating eval harnesses and observability/tracing tooling. * Comfort with ambiguity and unstructured problems, plus clear communication -- able to explain technical trade-offs to clinical and product stakeholders. * A degree in computer science, software engineering, or a related field. * Proficiency in English and German language. * Note on experience: we are looking for years of strong software engineering plus recent, hands-on agent work. Agent engineering is a young discipline, and we are not expecting a long tenure in it. Desirable Requirements * Healthcare/clinical-software and data exposure. * Strong foundation in statistics and probability theory. * Awareness of MDR and EU AI Act implications for clinical AI (high-risk classification, human oversight, data governance, post-market monitoring). * Deep cloud platform knowledge (especially AWS) and infrastructure-as-code. * Model adaptation or fine-tuning. * Familiarity with guardrail and safety frameworks for LLM applications. * Proficiency in Italian language. ## Description Join us as our AI Engineer (m/f/d) - Healthcare IT at Dedalus, one of the World's leading healthcare technology companies, in Graz to do the best work of your career and make a profound impact in providing better care for a healthier planet. You will contribute to the engineering behind our agentic AI roadmap: designing, building, configuring, evaluating, and operating production-ready agents that work reliably and safely in a regulated healthcare environment. This is an application- and systems-layer engineering role. Your success is measured by delivering agents that survive contact with real users and real clinical workflows. You will work closely with clinical, product, and domain experts, but your core craft is shipping and qualifying agentic systems, building the evaluation infrastructure, and optimizing the cost and latency of running them at scale. What you'll do * Build or configure production-ready agents. Design and ship multi-step, tool-using agents with memory and state for clinical and operational workflows. Own them end-to-end, including orchestration, and integration. Support our delivery and operation teams with deployment, and finding and fixing the root causes of failures. * Own the evaluation harness. Build and maintain offline eval datasets, combine LLM-as-judge with deterministic checks, and wire regression gates into CI so quality is enforced before release -- not discovered in production. * Design and optimise RAG. Build retrieval pipelines over structured and unstructured clinical text: chunking, embedding and retrieval strategy, reranking, and grounded/cited generation. Measure retrieval quality (faithfulness, context relevance, recall), not just end-output vibes. * Qualify agents for a regulated setting. Define acceptance criteria and failure-mode analyses; design guardrails, human-in-the-loop, and escalation paths; and produce the validation and verification evidence and traceability expected under Medical Device Regulation (MDR) and the EU AI Act for clinical AI. * Instrument and observe in production. Enable tracing every step an agent takes -- LLM calls, tool calls, retrieval steps -- monitor for quality drift, and close the loop by turning production failures into new eval cases. * Optimise cost and latency. Treat cost-per-task as a first-class metric. Apply model routing/selection, caching, token budgeting, batching, and prompt efficiency. ## Related Videos - [AI in High-Stakes Industries: Lessons Learned](https://www.wearedevelopers.com/videos/100253-ai-in-high-stakes-industries-lessons-learned) - [HTTP headers that make your website go faster](https://www.wearedevelopers.com/videos/1676-http-headers-that-make-your-website-go-faster) - [API Design - Getting Started](https://www.wearedevelopers.com/videos/33-api-design-getting-started) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Agentic AI Systems for Critical Workloads](https://www.wearedevelopers.com/videos/1592-agentic-ai-systems-for-critical-workloads) - [Event based cache invalidation in GraphQL](https://www.wearedevelopers.com/videos/433-event-based-cache-invalidation-in-graphql) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)