Job Description

Aarhus, Denmark

Are you an experienced AI/ML engineer who wants to help us shape how we build, test and operate AI? We're a strong software organization looking for someone to mature our AI practice across our products, while a talented team grows around you.

Stakeholder Intelligence is a unit within Rambøll, with SaaS products including SurveyXact, PeopleXact and Heart&Mind. These products help organizations across the Nordics and Europe understand and improve the lives of the people who matter most to them, whether customers, patients, citizens, employees or children. For some, that means listening to stakeholders and turning their feedback into insight they can act on. For others, it means giving managers what they need to improve the working lives of their teams, or supporting practitioners in daycare and schools in their work on quality and children's development.

We work with sensitive data, and keeping it safe shapes how we build: we primarily run models on our own GPU servers, on-prem. We're expanding our SaaS portfolio with AI-driven value creation, and we're looking for a senior engineer to help us do it responsibly and at scale.

Where you fit in

We're early in our AI journey. We have a mature software organization (50 developers and designers) with deep strength in traditional software engineering, and a Platform Engineering team that builds the shared platform our product teams build and run on. But ML and LLM work is new to us, and our practices for evaluating, testing and operating these systems are still forming. We've recently begun building a dedicated AI Engineering function; it's still small, and much of its work happens alongside Platform Engineering.

AI Engineering at Stakeholder Intelligence is a shared craft. Our AI Engineers are talented but early in their careers. Each is primarily attached to a product team and works closely with that team, while spending a significant portion of their time on shared methods and tooling across products. As our senior engineer, you'll spend even more of your time working broadly across products, and your job is to shape that craft.

You won't own product direction; that stays with the product teams and their Tech Leads. What you'll shape is how we do ML/LLM engineering: the methods, standards and practices for how we develop, test, evaluate, deploy and operate the AI parts of our products. You'll provide direction, bring proven experience, and represent AI engineering in cross-functional discussions with Product Managers, Tech Leads and other senior voices.

This is a hands-on, individual-contributor role with real influence, scoped to the experience you bring.

What you'll do

• Define our evaluation practice: how we determine whether one model, prompt or system genuinely outperforms another, drawing on methods such as golden datasets, deterministic checks, reference-based metrics, LLM-as-a-judge and human evaluation. This is central to the role.
• Establish our MLOps foundations and optimize how we serve our on-prem, open-source LLMs, working closely with Platform Engineering: deployment pipelines, model and prompt versioning, evaluation harnesses, monitoring (latency, accuracy, drift), reproducibility, and the shared tooling and standards that help good practice spread across teams.
• Build production-grade LLM features hands-on, in close collaboration with Product Managers, Designers, Tech Leads and Software Engineers.
• Mentor our AI Engineers and set the patterns and best practices that define how we do AI across the team.
• Engage with subject-matter experts and customer-facing teams to ensure AI is used in ways that are meaningful, secure and scalable.

What we're looking for (must-haves)

• Several years of hands-on experience building and operating ML/LLM systems in production, ideally in a SaaS or product context.
• Several years working with evaluation and MLOps frameworks, enough to have real opinions about how to evaluate LLM systems: golden datasets, deterministic and reference-based tests, LLM-as-a-judge and its pitfalls, and regression analysis when comparing models.
• Solid MLOps practice: deployment, lifecycle management and monitoring in production.
• Deep, hands-on experience taking prompt engineering and RAG well beyond proof-of concept into systems that perform reliably in production.
• Hands-on experience with AI agents, including their failure modes and a realistic sense of when they're the right tool.
• Proficiency in Python (Java a plus).
• Strong interpersonal skills and a genuine mentoring mindset. You share direction and experience generously, and you lift the people around you without taking over.
• Comfort building foundations in a low-maturity environment.
• A strong commitment to data security, guardrails and responsible, ethical AI.

Strong advantages

• A track record of establishing ML/LLM practice from scratch (evaluation frameworks, testing discipline, MLOps foundations), rather than only working within practices someone else built. Ideally across more than one team or product.
• Hands-on experience running and serving models locally on your own GPU infrastructure, not just using open-source models through a hosted API.
• Experience fine-tuning open-source LLMs to make them more domain-specific.
• Experience translating emerging research into practical applications.

What we offer

• A foundation-owned company with a clear purpose to create a better world.
• Competitive salary.
• Support for your personal and professional development from day one, in an environment of professional exchange and learning among peers.
• A hybrid working model balancing on-site presence with remote flexibility.
• Health insurance and pension scheme.
• A sixth week of holiday.
• A strong social community with regular social events in a dynamic environment.
• An attractive lunch scheme in perhaps Aarhus' best canteen.

How to apply

Apply online with your CV and a cover letter. We expect you to understand, speak and write Danish (minimum CEFR B1, preferably B2 or higher) and to work on-site in Aarhus at least 3 days a week.

We'll be conducting interviews in the second half of August and through September, so please send your application before then to be sure it's considered. Reach out to our Head of Software Engineering, Rasmus Hallenberg-Larsen, at rah@ramboll.com if you'd like to know more about the role