Help us create the software platform that will change Norwegian healthcare forever.

Location: Oslo, Bergen, Bodø, Stavanger, or from anywhere in Norway

Salary: 1.150-1.350 MNOK/year

🌍 Why this role matters

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.

As municipalities face growing pressure to deliver more care with fewer resources, time saved in daily workflows has become one of the most valuable outcomes we can deliver. We’re building toward a future where healthcare staff have an “always available” digital colleague that helps them find the right information at the right time, reduces repetitive administrative work, and supports better decisions with clarity and consistency.

As a Senior AI Engineer, you’ll help build the shared AI capabilities that make this possible. This includes speech-to-text and ambient documentation, source-lined summaries and “as the journal” experiences, safe automation and agentic workflows, and the guardrails that ensure everything is traceable, secure and trustworthy in production.

This isn’t about experimenting with AI for its own sake. It’s about building reliable, auditable automation that gives healthcare professionals time back for what matters most: caring for people.

🔧 What you’ll be working on

You’ll help build the shared automation and AI capabilities that power Aidn’s “digital colleague” direction. This work sits at the intersection of workflow orchestration, safe automation, and applied AI, and is focused on turning real municipal workflows into measurable time saved.

Your work will include projects such as:

Workflow orchestration as a foundation: Designing and evolving the workflow engine that structures how work moves through Aidn. This includes version-aware state machines, observability, audit trails, and production-grade reliability so workflows can safely anchor automation.
Automation Engine and guardrails: Building the automation layer that performs real, auditable actions inside workflows. This includes safe execution patterns, previews before actions, human-in-the-loop checkpoints, risk tagging, rollback capabilities, and full traceability.
Applied AI inside structured workflows: Integrating capabilities like speech-to-text, summarisation, drafting assistance, and contextual question-answering directly into workflows — always tied to the correct patient, case, and data context.
Agentic and multi-step task flows: Exploring controlled, goal-driven automation where the system can propose and chain steps such as preparing a case, drafting a letter, creating follow-up tasks, or updating plans — with strict safety boundaries and transparency.
Shared AI and automation building blocks: Creating reusable components, APIs, evaluation patterns, and tooling that allow other product teams to adopt automation and AI safely, without reinventing core infrastructure.
Evaluation, monitoring, and continuous improvement: Establishing methods to test, measure, and monitor automated and AI-driven workflows over time, ensuring quality, reliability, and trust as usage scales.

This is a role for someone who wants to build AI and automation that actually ships — in a domain where safety, traceability, and user trust are non-negotiable, and where the impact is measured in time returned to patient care.

🛠️ Current Tech Stack

Aidn’s automation and AI capabilities are built on modern, production-proven technologies, with a strong focus on reliability, traceability, and safe execution in healthcare workflows. We build AI systems that are meant to run in production, be observable, and be trusted by teams and municipalities.

We use:

Languages & Frameworks: .NET / C# for core backend services, python for AI and evaluation tooling
Cloud & AI Platform: Microsoft Azure, including Azure AI services and Azure AI Foundry
Workflow & Automation: A shared workflow engine (state-machine based) and an automation platform with built-in guardrails
Data & Storage: PostgreSQL, Azure Storage, and structured JSON-based payloads
Messaging & Streaming: Kafka for reliable event-driven workflows and system integration
Infrastructure & CI/CD: GitHub Actions, ArgoCD, Terraform and Atlantis
Observability: Loki, Grafana, Tempo, and Mimir (the LGTM stack), with monitoring patterns that support both workflows and AI quality
Collaboration & Tooling: RFC-based documentation process, shared technical PRDs, and Slack for cross-team collaboration

We believe in pragmatic engineering: building the thinnest safe slice that delivers measurable value, and evolving it through real-world usage, strong guardrails, and continuous improvement.

🤝 What we're looking for

We’re looking for a hands-on AI engineer who combines strong software engineering fundamentals with real experience applying AI in production systems. You care about building capabilities that are safe, observable, and trustworthy—not just impressive prototypes.

You understand that in healthcare, automation requires guardrails, traceability, and human oversight. You’re motivated by building shared AI capabilities that other teams can confidently adopt and extend. You take ownership of outcomes, care about measurable impact, and know how to balance experimentation with reliability and operational discipline.

You enjoy working closely with product teams and see developer enablement as part of the role—not an afterthought.

🧠 Must-Haves

Strong software engineering experience building and operating production systems
Hands-on experience working with LLMs and/or speech-to-text in real products
Experience taking AI capabilities from prototype to production, including reliability, latency, cost, and failure handling
A quality and evaluation mindset, with experience measuring system behavior and preventing regressions
Familiarity with cloud platforms (preferably Azure) and modern observability tooling
Strong communication skills and the ability to collaborate across teams and disciplines

🌟 Nice-to-Haves

Experience building agentic systems or multi-step AI workflows
Experience monitoring AI systems in production (quality, cost, latency, drift, failure modes)
Familiarity with Azure AI Foundry or similar model deployment and governance tooling
Experience building shared internal platforms used by multiple product teams
Experience working in regulated environments where traceability, auditability, and risk management are required
Familiarity with workflow orchestration systems, state machines, or event-driven architectures

💬 Personal Attributes

Strong Communicator & Collaborator: Able to work well with diverse teams and build solutions from the ground up.
Service-Oriented Mindset: Puts the needs of colleagues and customers first, supporting others to achieve shared goals.
Problem Solver with High Agency: Takes initiative, moves work forward without waiting to be told, and finds pragmatic solutions even when the path isn’t clear.
Honest & Direct: Values transparency and clarity in all interactions.

Good Sense of Humor: Keeps the atmosphere positive and fosters a collaborative, enjoyable work environment.

🔍 You’re likely motivated by…

Tackling Hard Problems – You thrive on solving complex challenges where reliability, safety, and real-world constraints matter, and where the right solution requires both strong engineering and good judgment.
Reducing Complexity – You take pride in simplifying systems, creating clean abstractions, and building platforms that are easy for others to understand, operate, and extend.
Building AI that ships – You want to move beyond prototypes and deliver AI capabilities that work reliably in production, with evaluation, monitoring, and guardrails that make teams confident using them.
Enabling Others – You enjoy building shared tools, patterns, and workflows that help other developers move faster, avoid pitfalls, and ship AI-enabled features with confidence.

Delivering Real Impact – You’re motivated by measurable outcomes, like minutes saved in high-volume municipal workflows, less administrative drag for healthcare staff, and more time returned to patient care.

✅ What success looks like

In 3 months:

You’ve developed a strong understanding of Aidn’s workflow engine, automation platform, and the safety and governance requirements for shipping AI in healthcare.
You’ve taken ownership of a defined area of the platform — such as workflow runtime, automation building blocks, evaluation tooling, or monitoring — and delivered meaningful improvements.
You’ve built trust across teams by communicating clearly, collaborating effectively, and consistently following through on commitments.

In 6 months:

You’re independently delivering high-quality AI and platform capabilities that are used in real workflows in production or late-stage rollout.
You’ve contributed to shipping at least one AI-enabled workflow step with clear guardrails, traceability, and operational visibility.
You’ve helped establish or improve evaluation and monitoring practices so AI behavior can be measured, understood, and improved over time.
You’re recognized as a reliable problem-solver who balances experimentation with safety, and speed with long-term maintainability.

In 12 months:

You’ve contributed materially to launching a “digital colleague” workflow in production that demonstrates measurable time saved for municipal staff.
Multiple teams are successfully adopting and extending shared automation and AI capabilities using the patterns and tooling you’ve helped build, with minimal direct support.

Aidn has a stronger and more scalable foundation for AI-driven automation, including clearer evaluation standards, better observability, and more consistent engineering practices across teams.

💙 Why you’ll love working here

🔑 High trust, real ownership

You’ll be trusted to make decisions, take initiative, and shape your own path. There’s little bureaucracy, and we hire people we trust to figure things out.

🚀 Growth that actually means something

You’ll be joining a growing Platform organization at a pivotal stage, with the chance to leave a real mark on how our backend systems evolve. As Aidn scales, your opportunities to lead, mentor, and influence technical direction will grow alongside it.

🧠 Support to get better

You’ll have regular 1:1s, feedback, and mentoring from experienced engineering leaders. We support personal development through learning budgets, conference access, and time for deep work.

🧘 Flexibility that respects your life

Work from home, from one of our local offices, or take a workation if you need a change of scenery. We trust you to manage your time in a way that works for you and your team.

🎁 Perks and benefits

6 weeks vacation
Flexible location and working hours
Employee shares—you can own a piece of what you build
A collaborative, caring team that values both kindness and excellence

Aidn is part of the Kernel cooperation, where we build technology for the next generation of welfare societies. With financial backing in order, we are privileged to be a fully autonomous startup busy building our company from the ground up the way we see fit.

Aidn recognizes and celebrates diversity in all its forms, visible and non-visible in all areas of the work environment. We work to promote an anti-discriminatory environment where everyone feels safe and welcome.

Read our full Diversity, Inclusion & Belonging policy in our handbook here.

Are you curious? We welcome you to check out our employee handbook to get to know us, some of our benefits, and what drives us.