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Lead Enterprise Architect (AI/ML, Remote)

Copenhagen, Capital Region
Posted 2 weeks, 3 days ago
Engineering

About the role

Job summary

This position focuses on guiding the strategic direction, design, and implementation of AI technologies to align with business objectives and enhance customer value. The role is essential for fostering innovation and leveraging AI-driven solutions to address supply chain challenges.

Qualifications

  • Expertise in AI/ML systems, generative AI, and agentic AI frameworks.
  • Experience in developing AI agents, frameworks, and tools, with a focus on architectural and coding standards.
  • Strong background in architecting large-scale distributed systems emphasizing reliability, performance, and security.
  • Familiarity with large language models and orchestration frameworks such as ReAct and LangChain.
  • Proficiency in programming languages including Python, R, or Java.
  • Advanced degree (PhD or Master’s) in Computer Science, AI/ML, Operations Research, or a related field.

Responsibilities

  • Define and enhance the architecture for the Enterprise AI Platform, including frameworks for agent orchestration and lifecycle management.
  • Architect data flywheels to promote continuous improvement of AI agents through feedback and iteration.
  • Make architectural decisions regarding deployment models (on-prem, cloud, hybrid) to ensure scalable and efficient solutions.
  • Conduct design reviews, create technical documentation, and mentor engineers on architecture and coding principles.
  • Promote observability, monitoring, and telemetry for trustworthy AI solutions.
  • Advocate for data governance and ethical AI practices, ensuring compliance with regulations.
  • Collaborate with IT, business units, and external partners to integrate AI into core processes.
  • Stay updated on industry trends in AI and data technologies to assess their impact on the business.

Skills

  • Excellent collaboration and communication skills to influence stakeholders and convey complex concepts.
  • Strong understanding of security, governance, and compliance in enterprise AI contexts.

Tools

  • Familiarity with APIs, data connectors, and integration of enterprise platforms such as ERP and CRM.
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