Lead Engineer - Data & AI

About the role

A.P. Moller - Maersk is looking for a Lead Engineer - Data & AI to join its team in Copenhagen. This is a senior individual contributor role with no direct reports, focused on designing and building the most critical parts of the company's data and AI platform. The scope covers the full stack: storage and table formats, pipelines, services, applications and the agentic systems built on top.

Key responsibilities

• Own the architecture of major platform components and data products, from storage through to the application and agent layers.
• Define target architectures and migration paths, with attention to open standards, interoperability and long-term flexibility.
• Write and deploy production code, personally building the difficult components and reference implementations.
• Make build vs. buy decisions with clear reasoning on cost, operational load and dependency risk.
• Design platform foundations: data contracts, lineage, schema evolution, access control and cost management.
• Set the agentic engineering practice across the group, including internal tooling, MCP servers, reusable skills, evaluation harnesses and code review standards.
• Partner with Product, Business stakeholders and Engineering Managers on sequencing, trade-offs and technical risk.

Required profile

• Demonstrated work: systems designed and built that run in production, with explanations of the decisions made.
• Track record as a Staff, Principal or Lead engineer who stayed hands-on, or as an architect who still writes and deploys code.
• Experience as the technical anchor on a platform or product used by other teams.
• Public body of work such as an active GitHub profile, open source contributions, technical writing or talks.
• Ability to move between architectural thinking and detailed implementation within the same week.

Required skills

• Programming: Python, R and SQL, with deep expertise in at least one language.
• AI and ML: agentic coding, LLM systems in production, machine learning pipelines and deployment.
• Data engineering: table formats (Iceberg, Delta Lake, Hudi), batch and streaming architectures, dbt, data modelling, multi-tenant governance and lineage.
• Software engineering: service and API design, observability, infrastructure as code (Terraform, Bicep) and Kubernetes.
• Open source exposure and familiarity with open standards.