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Help us create the software platform that will change Norwegian healthcare forever. Location: Oslo, Bergen, Bodø, Stavanger, or from anywhere in Norway Salary: 1.2-1.3 MNOK/year 🌍 Why this role matters Somewhere in Norway right now, a nurse is filling in a form instead of caring for someone. As municipalities face growing pressure to deliver more care with fewer resources, time saved in daily work has become one of the most valuable things we can deliver. At Aidn, we’re creating the missing healthcare platform all Norwegians deserve: an integrated set of services and applications that allow municipal institutions, healthcare professionals, social workers, and citizens to collaborate on healthcare matters as a single team. We're building toward a future where healthcare staff have an always-available digital colleague: one that surfaces the right information at the right time, cuts repetitive admin tasks and supports better decisions. As a Senior AI Engineer, you'll build and operate the AI platform the rest of Aidn runs on, the production services, guardrails, and shared components that other teams use to build their own AI features. The distance between AI that's impressive in a demo and AI a clinician will trust on a shift is real. Closing that gap, by making it reliable, observable, and safe enough for healthcare, is the job. Getting it right means a nurse gets her afternoon back. Getting it right at scale, across every municipality we serve, means thousands of hours returned to patient care, and Norway a step closer to a healthcare system that helps people live the longest, healthiest lives possible. What you’ll be working on You'll build and operate the shared AI capabilities behind Aidn's digital colleague direction. The work sits where applied AI, safe automation, and platform engineering meet, and it's measured in real time returned to patient care. Your work will include things like: Shared AI building blocks: Creating the reusable components, APIs, evaluation patterns, and tooling that let other product teams adopt AI safely, without reinventing core infrastructure. This is the heart of the role. Applied AI into production: Taking capabilities like speech-to-summary, form-filling, and contextual drafting from pilot to production, tied to the correct patient, case, and data context, with real attention to latency, cost, reliability, and failure modes. Guardrails for healthcare: Building safe execution into the platform: previews before actions, human-in-the-loop checkpoints, risk tagging, rollback, full traceability and audit trails, and the separation between MDR and non-MDR contexts. Evaluation and observability: Establishing how we test, measure, and monitor AI behaviour over time, eval frameworks, ground-truth validation, regression detection, and production monitoring, so quality and trust hold as usage scales. Agentic and multi-step flows: Helping shape controlled, goal-driven automation, proposing and chaining steps like preparing a case or drafting a letter, with strict safety boundaries. This is emerging work as the platform matures. Current Tech Stack We build on modern, production-proven technology, chosen for reliability, traceability, and safe execution in healthcare. The result is AI that teams and municipalities can actually trust. We use: Languages & frameworks: Python for AI platform and evaluation tooling, C# / .NET for core backend services. Cloud & AI platform: Microsoft Azure, including Azure AI services and Azure AI Foundry Data & storage: PostgreSQL and Azure Storage Messaging & streaming: Azure Event Hub (Kafka) Infrastructure & CI/CD: GitHub Actions, ArgoCD, Terraform Observability: LGTM stack (Loki, Grafana, Tempo, and Mimir) You don't need every item on this list. You should be comfortable working across a stack like this and confident picking up what you don't already k