We are looking for a senior AI Engineer for our client in the pharma industry:

This is a senior, hands-on AI engineering position based in the Copenhagen area, embedded within the clients core product team alongside fullstack, frontend, backend, and DevOps engineers.

The primary purpose of this role is twofold:

1. Institutional AI knowledge ownership: ensuring that the deep AI engineering expertise being developed through the Microsoft partnership is embedded within Novo Nordisk, not lost to it. This person will be the internal anchor for all AI engineering decisions, patterns, and best practices as the product scales.

2. European timezone AI engineering lead: serving as the daily, in-timezone connection point between the Novo Nordisk development team in Copenhagen and the Microsoft AI Acceleration Studio teams based in the US and London, enabling continuous, well-integrated AI development across the full team.

This is a rare opportunity to do advanced AI engineering in a regulated pharma environment, working on a product that directly impacts drug development decisions and to help architect the self-learning systems that will define the next generation of clinical AI tooling .

Key Responsibilities

• Co-own and evolve the AI agent architecture in close collaboration with Microsoft’s AI Acceleration Studio, ensuring the client builds deep internal capability and understanding of every architectural decision

• Design, build, and maintain agentic tooling: including agent skills, tool definitions, code execution environments, sandboxed runtime environments, and dynamic code generation patterns

• Lead the engineering of the self-learning loop post-MVP: designing the feedback and evaluation infrastructure that allows the agent to improve continuously based on SME validation scores, usage data, and outcome metrics

• Serve as the European timezone AI engineering anchor, ensuring seamless handovers, daily continuity, and tight integration between the Copenhagen-based development team and the US/London Microsoft teams

• Translate AI engineering best practices into the broader development team: upskilling fullstack, backend, and DevOps colleagues on agentic patterns, LLM integration, and evaluation frameworks

• Own and evolve the evaluation framework for the agent: including system performance metrics, query accuracy metrics, LLM-as-Judge evaluators, and SME scoring pipelines

• Collaborate on prompt engineering, reasoning chain design, and agent iteration: working at the frontier of what reasoning agents can do in a clinical data environment

• Ensure production-grade AI engineering standards: including observability, logging, safety guardrails, responsible AI practices, and compliance with pharma regulatory requirements

• Contribute to the multi-modal expansion of the agent beyond R&D into commercial data domains, competitive intelligence, and portfolio-level intelligence

Required Skills

Must-Have Qualifications

• 6+ years of software engineering experience, with a significant and demonstrable focus on AI/ML engineering in production environments
• Deep, hands-on expertise in building agentic AI systems — including agent architectures, tool/skill design, code execution environments, and multi-step reasoning pipelines

• Strong proficiency in LLM integration and prompt engineering, with practical experience working with large-scale language models in enterprise or regulated settings

• Experience designing and operating sandboxed code execution environments for AI agents — understanding the security, reliability, and performance considerations involved

• Solid software engineering foundations — proficiency in Python (essential), with strong understanding of APIs, data pipelines, cloud infrastructure, and CI/CD practices

• Experience building evaluation frameworks for AI systems — including automated metrics, LLM-as-Judge patterns, and human-in-the-loop validation workflows

• Proven ability to work in distributed, cross-timezone engineering teams, with excellent asynchronous communication skills and structured handover practices

• Fluency in English, written and verbal

Highly Valued Experience:

• Experience working with or within large enterprise AI partnerships (e.g., Microsoft, Google, AWS AI teams) — ability to engage as a peer with senior AI engineers at that level

• Familiarity with clinical data environments, biostatistics tooling, or pharmaceutical R&D data platforms

• Experience with SQL generation, statistical analysis automation, or scientific computing in an AI-assisted context

• Background in RAG (Retrieval-Augmented Generation), knowledge grounding, or domain-specific fine-tuning

• Exposure to responsible AI, AI governance, or regulated industry AI deployment

• Experience contributing to or building self-improving or continuous learning AI systems

• Familiarity with Azure AI, Azure OpenAI Service, or the Microsoft AI platform stack

Start date: 20-04-2026

End date: 19-04-2027

Location: Onsite DK

For the duration of this assignment Ework Services (0,9%) will be deducted from the total amount invoiced.

We offer candidates continuously. This means that we sometimes remove the assignments before deadline. If you are interested, we recommend that you apply immediately.