Principal AI Engineer

We are building an AI-powered operating model for Ocean Operations and a next-generation optimisation capability for global Network Design. This role is not limited to agent orchestration or execution workflows. It is a senior technical leadership role for how AI strengthens optimisation methods modelling and decision quality across both planning and execution.
The remit is broader than a standalone AI feature set. It covers how AI optimisation and engineering come together to deliver better network decisions at scale while also shaping agentic decision systems in Control Tower style execution environments.

Role purpose:
The Principal AI Engineer will set the technical direction for AI-augmented optimisation and intelligent operational decisioning. This person will lead through specialists in optimisation modelling heuristics analytics and experimentation. They will define where AI can materially improve search structural recommendations and solver-backed workflows while also guiding the design of multi-agent systems for reasoning orchestration exception handling and human-in-the-loop execution.

What you will own
Technical direction for AI-augmented optimisation and agentic decision systems across Network Design and operational execution
How planning problems are framed and where AI can improve search candidate generation structural recommendations and decision quality
The interface between AI systems heuristics solver layers optimisation services and enterprise workflows
Patterns for when AI should recommend act autonomously or escalate to humans
The quality bar for experimentation evaluation and translation of new methods into measurable value for planners and decision-makers
Reusable intelligence patterns that can scale across both Control Tower and Network Design use cases

Key responsibilities:
Lead the optimisation intelligence agenda across network design routing vessel deployment cargo flow and operational decisioning
Set direction for how teams define problem structures formulate optimisation approaches design heuristics and identify where AI can improve search modelling quality and decision outcomes

Guide specialists building optimisation models analytical components and AI capabilities ensuring their work fits into a coherent AI plus OR architecture
Drive structured experimentation to compare alternative methods validate model behaviour and improve performance robustness and scalability
Define how AI systems should interact with solver layers heuristics and optimisation services rather than treating AI as a separate layer
Lead the use of agentic AI where it adds value to reasoning orchestration exception handling and human-in-the-loop decision support
Shape the innovation roadmap by identifying promising new methods and accelerating them into pilots and scaled capabilities
Raise standards across modelling practice technical quality experimentation discipline and architectural judgment
Communicate assumptions trade-offs and limitations clearly to senior stakeholders and decision-makers

What we are looking for
We are looking for a senior technical leader with deep credibility across both AI and optimisation and with the ability to lead through highly skilled specialists rather than doing all modelling work personally.
The strongest profile will combine experience in agentic AI systems and human-in-the-loop workflows with strong grounding in optimisation methods such as heuristics search mathematical programming decomposition or hybrid AI plus OR approaches. This person should also be comfortable directing teams working on modelling experimentation performance improvement and implementation into production environments.

Required qualifications:
Advanced degree in engineering mathematics computer science operations research or another quantitative field
Significant experience leading applied AI decision intelligence optimisation or analytical platform work in complex operational domains
Strong understanding of optimisation and modelling principles and how algorithms support real planning problems
Experience improving planning routing scheduling or network optimisation through heuristics analytics or AI-based enhancements
Strong Python skills and comfort working closely with software and data engineering teams
Proven ability to lead experimentation validate model performance and convert technical ideas into business-relevant capabilities
Strong communication skills with the ability to explain technical trade-offs to non-technical stakeholders

Preferred qualifications:
Experience in logistics supply chain shipping transportation or another optimisation-heavy industry
Exposure to large-scale network design routing fleet planning cargo flow optimisation or similar decision systems
Experience using AI or ML to improve optimisation performance such as learning-augmented algorithms surrogate models search guidance or reinforcement learning
Experience working across research prototyping and productionisation of optimisation methods

Leadership expectations:
This role is expected to lead through others. The Principal AI Engineer should create the technical direction decision framework and quality standards for a broader team of optimisation analytics and AI specialists. The role should not be framed as a lone architect writing models end to end. It should be framed as the senior leader who sets the optimisation intelligence agenda and ensures that specialist work compounds into a coherent capability.

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Maersk is committed to a diverse and inclusive workplace and we embrace different styles of thinking. Maersk is an equal opportunities employer and welcomes applicants without regard to race colour gender sex age religion creed national origin ancestry citizenship marital status sexual orientation physical or mental disability medical condition pregnancy or parental leave veteran status gender identity genetic information or any other characteristic protected by applicable law. We will consider qualified applicants with criminal histories in a manner consistent with all legal requirements.

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