Assoc Dir Data/AI Solutions Architect

Position Assoc Dir Data/AI Solutions Architect Job description Job description Location Ballerup, Denmark Job category Digital & IT Apply now Senior AI & Data Solutions Architect Location - Ballerup Your new role The Senior AI & Data Architect is responsible for designing scalable, secure, compliant, and value-driven AI and data solution architectures that enable successful execution of strategic AI initiatives. This role partners closely with AI Product Owners, Enterprise Architects, Platform teams and delivery leads (Data Scientists, Data & AI Engineers) to translate business needs into robust technical solution designs. The Senior AI & Data Architect ensures that AI and advanced analytics initiatives are technically feasible, strategically aligned, reusable, governed, and ready for enterprise-scale deployment. Your key responsibilities include: AI & Data solution architecture Lead the architecture design for complex AI, advanced analytics, and data product initiatives across Commercial functions. Translate business requirements and success criteria into scalable technical solution concepts, data flows, architecture designs, and implementation roadmaps. Partner with AI Product Owners to pressure-test use cases for technical feasibility, architectural complexity, data readiness, scalability, and delivery risks. Define solution architecture across data ingestion, data engineering, feature engineering, model development, model deployment, integration, monitoring, and user consumption layers. Ensure solutions are designed for scalability, maintainability, performance, security, and long-term operational ownership. Provide architectural guidance from ideation through deployment and value realization. 2. Technical leadership across AI delivery lifecycle Lead technical design discussions across Data Science, AI Engineering, Data Engineering, Platform, Architecture, Security, Legal, Privacy, Compliance, and business teams. Influence technology choices, architecture decisions, solution patterns, and delivery roadmaps for AI and data initiatives. Guide end-to-end technical delivery for strategic AI and data projects, ensuring alignment between business objectives, technical implementation, and enterprise standards. Identify technical dependencies, constraints, risks, and mitigation plans early in the use case lifecycle. Ensure technical designs are pragmatic, reusable, and aligned with delivery timelines and business priorities. 3. Data Architecture & Data Strategy Define data architecture for AI and analytics solutions, including data sourcing, integration, transformation, storage, quality, lineage, and access patterns. Assess data availability, quality, usability, ownership, and governance requirements for AI use cases. Collaborate with Data Owners, Data Stewards, Data Engineers, and business teams to ensure AI solutions are based on trusted and governed data. Recommend data product designs and reusable data assets that support multiple AI and analytics use cases. Promote standardization of data models, semantic layers, metadata, and data governance practices. 4. AI Architecture, MLOps & GenAI Enablement Define architecture patterns for Machine Learning, Generative AI, Natural Language Processing, predictive analytics, optimization, and decision-support solutions. Guide model development and deployment approaches, including experimentation environments, model lifecycle management, monitoring, retraining, and performance tracking. Ensure implementation of MLOps and LLMOps practices for production-grade AI solutions. Define architectural guardrails for prompt engineering, retrieval-augmented generation, vector databases, model selection, evaluation, observability, and human-in-the-loop controls. Support teams in selecting appropriate AI models, platforms, frameworks, APIs, and integration approaches. 5. Enterprise Architecture, Governance & Compliance Ensure all AI and data solution designs align with enterprise architecture principles, technology standards, security patterns, compliance requirements, and governance frameworks. Recommend standards, best practices, and governance mechanisms for AI solution development and deployment. Partner with Enterprise Architecture, Cybersecurity, Legal, Privacy, Compliance, and Responsible AI teams to ensure solutions meet internal and external requirements. Document architecture decisions, solution designs, design trade-offs, risks, and governance approvals. Support architecture review boards and technical governance forums. Promote consistent use of approved platforms, reusable components, integration standards, and deployment patterns. 6. Stakeholder Engagement & Cross-Functional Collaboration Communicate complex technical concepts in clear business language to senior stakeholders. Facilitate technical workshops for solution design, data discovery, architecture alignment, integration planning, and acceptance criteria definition. Prepare architecture recommendations, solution options, risk assessments, and technical decision papers for leadership discussions. 7. Reusable Frameworks, Standards & Capability Building Develop and maintain reference architectures, reusable design patterns, architecture playbooks, and technical guardrails for AI and data solutions. Identify opportunities to standardize and industrialize repeated AI delivery patterns. Mentor Data Scientists, Data Engineers, AI Engineers, Solution Architects, and technical leads on architecture best practices. Maintain up-to-date knowledge of industry trends, advancements in AI, data technologies, cloud platforms, and architecture practices. Your new department: This role partners closely with AI Product Owners, Enterprise Architects, Platform teams and delivery leads (Data Scientists, Data & AI Engineers) to translate business needs into robust technical solution designs. The Senior AI & Data Architect ensures that AI and advanced analytics initiatives are technically feasible, strategically aligned, reusable, governed, and ready for enterprise-scale deployment. The role is critical in de-risking AI delivery by guiding technology choices, architecture decisions, data strategy, integration patterns, platform usage, Responsible AI controls, and solution scalability across the AI use case portfolio. This directly complements the AI Product Owners role, which focuses on business problem framing, prioritization, stakeholder engagement, and value realization. Skills & Qualifications. Bachelor’s degree in computer science, Engineering, Data Science, Information Systems, Applied Mathematics, Statistics, or a related field. Master’s degree in data science, AI, Computer Science, Engineering, Business Analytics, or equivalent experience preferred. Architecture, cloud, data, AI, or security certifications are an advantage. 10+ years of experience in AI/data solution architecture, enterprise architecture, data engineering, analytics, or digital solution delivery. 5+ years of experience designing and delivering data, analytics, AI, Machine Learning, or cloud-based solutions. Proven experience architecting enterprise-scale data and AI solutions in complex, matrixed organizations. Experience working with senior business stakeholders and technical teams to translate business needs into scalable technical designs. Experience with data platforms, cloud architecture, APIs, data integration, data governance, security, privacy, and production-grade AI deployment. Experience guiding cross-functional teams across Data Science, Data Engineering, AI Engineering, Platform, Security, Architecture, and business functions. Working at Novo Nordisk Every day we seek the solutions that defeat serious chronic diseases. To do this, we approach our work with determination, constant curiosity and a commitment to finding better ways forward. For over 100 years, this dedication has driven us to build a company focused on lasting change for long-term health. One where diverse thinking, shared