Senior AI Engineer — Production‑Driven Health AI

Novo Nordisk

Ballerup

On-site

DKK 743,000 - 1,092,000

Full time

4 days ago

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Job summary

Novo Nordisk invites applications for a Lead AI Engineer in Denmark to translate strategic AI use cases into reliable products that create value across Novo Nordisk and for patients. You will guide complex AI deployments, collaborate with product managers and domain experts, and ensure quality through CI/CD and observability practices.

You will work in a cloud-native, cross-functional setup with strong emphasis on responsible AI, data integrity and scalable software delivery.

Qualifications

8+ years of experience building AI/ software products.
Experience taking AI-centric applications from idea to production in a product or platform team.
Hands-on experience operating AI in production with evaluation, monitoring, reliability and cost-performance trade-offs.
A sound foundation in statistical and machine-learning concepts with practical experience of modern AI patterns.
Strong programming and software engineering skills, including testing, APIs and CI/CD, preferably with GitHub.
Experience using AI-assisted development tools and coding agents as part of software delivery.
Knowledge of model and tool integrations, MCP and observability approaches.
Product sense to clarify needs, shape solutions and explain technical choices to diverse audiences.
Ability to set technical direction across multiple AI deliveries and raise engineering standards through reusable patterns.
Collaborative mindset, self-motivation and curiosity about evolving AI field.
An MSc, PhD or similar degree in engineering, computer science, data science, statistics, ML or related field; a BSc with industry experience will be considered.

Responsibilities

Take AI- and ML-centric use cases from discovery through production in cross-functional product teams across Commercial, Manufacturing & Product Supply and strategic initiatives.
Engineer the product-specific intelligence layer — data and feature transformations, decision logic, model integrations, knowledge grounding, user-facing services, human-oversight controls and fallback behaviour.
Partner with product managers and domain experts to frame problems, agree priorities and define meaningful product outcomes.
Use AI-assisted development tools productively while maintaining high standards for code quality and review.
Build reliable, scalable, secure and observable solutions with testing, evaluation, documentation and CI/CD practices.
Apply Responsible AI, data integrity, traceability and auditability in a regulated pharmaceutical environment.
Collaborate with the AI Foundation team to reuse shared capabilities and turn delivery lessons into reusable components and patterns.