AI/ML Engineer (Data Engineering + AI Focus)

A.P. Moller - Maersk is the global leader in container shipping services. The business operates in 130 countries and employs 80,000 staff. An integrated container logistics company, Maersk aims to connect and simplify its customers’ supply chains.

Today, we have more than 180 nationalities represented in our workforce across 131 Countries and this mean, we have elevated level of responsibility to continue to build inclusive workforce that is truly representative of our customers and their customers and our vendor partners too.

The team - who are we:

We are an ambitious team with the shared passion to use data, data science (DS), machine learning (ML),

advanced simulation, optimization and engineering excellence to make a difference for our customers.

We are a team, not a collection of individuals. We value our diverse backgrounds, our different personalities and

strengths & weaknesses. We value trust and passionate debates. We challenge each other and hold each other

accountable. We uphold a caring feedback culture to help each other grow, professionally and personally.

We Offer - This Is What You Get

You will be part of the APM Terminals team within Global data and analytics (GDA). We believe data and AI are crucial to how we will operate, grow, and shape our industry’s future. Our Data and AI Foundation is the platform making this possible - unifying trusted data across our business and fuelling advanced analytics and AI solutions. It’s central to our ambitious journey of becoming a truly data-driven, AI-enabled organization.

We’re looking for a hands-on Engineering Manager to build and scale this foundation. This is a unique opportunity for someone who’s as comfortable diving into code and architecture as guiding a team - a leader who can inspire great engineers while ensuring AI is built into the core of our platform.

What you’ll do

• Lead the development of a global Data and AI platform - the backbone of data & AI innovation across APM Terminals.
• Act as a hands-on technical leader - leading by example, owning architecture decisions, writing key parts of the codebase, and tackling complex problems alongside your team.
• Build for reuse and scale - designing shared data models, KPIs, pipelines, and APIs that empower teams across APMT to innovate faster.
• Infuse the platform with intelligence - applying AI to improve data quality, automate monitoring, and boost developer productivity.
• Advance our ML / AI Ops capabilities - ensuring seamless model training, deployment, monitoring, and lifecycle management for reliable AI in production.
• Empower teams to build and scale AI & GenAI solutions - by providing trusted data and robust, production-ready infrastructure.
• Work with product, architecture, and business stakeholders - to turn real needs from operations, commercial, finance, and more into durable platform features.
• Coach and mentor a talented engineering team - inspiring a high bar for quality, ownership, and continuous improvement.

What makes this role stand out

• Lead + build - you get to manage a team and remain deeply technical (no need to choose one or the other).
• Intelligent platform - the foundation gets smarter with AI to constantly improve quality, speed, and scale.
• Broad impact - your technical decisions and code will accelerate innovation across the company, enabling many teams to create value faster.
• Global challenge, real impact - you’ll tackle complex, cross-domain problems at global scale, and see your solutions make a lasting difference.

What we’re looking for

• Deep engineering experience - strong background in data engineering, including designing and operating large-scale data and AI platforms.
• Proven leadership - you’ve managed and developed engineers while staying technical (architecture, code reviews, even coding when needed).
• Modern architecture skills - solid understanding of cloud-native data architectures, platform engineering, and best practices in building robust, scalable data systems.
• AI/ML exposure - hands-on experience with AI/ML or data science systems in production, and familiarity with MLOps / AI Ops practices to support model deployment and monitoring.
• Automation & quality mindset - experience with AI-assisted development or intelligent automation for data quality/observability.

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.

We are happy to support your need for any adjustments during the application and hiring process. If you need special assistance or an accommodation to use our website, apply for a position, or to perform a job, please contact us by emailing [email protected].

Programming: Writing code to manipulate, analyze, and visualize data, often using languages like Python, R, and SQL.

Proficiency Level: Proficient

AI & Machine Learning: Creating systems that can perform tasks that typically require human intelligence. Using Machine learning (ML), a subset of AI that uses algorithms to learn from and make predictions based on data

Proficiency Level: Proficient

Data Analysis: Inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making

Proficiency Level: Foundational

Machine Learning Pipelines: Using automated workflows that manage the end-to-end process of training and deploying machine learning models.

Proficiency Level: Proficient

Model Deployment: Making a trained machine learning model available for use in production environments.

Proficiency Level: Proficient

SPECIALIZED SKILLS

Big Data Technologies: Using continuous integration and continuous delivery (CI/CD) pipelines to automate the process of software development, including building, testing, and deploying code

Natural Language Processing (NLP): Focusing on the interaction between computers and humans through natural language.

Data Architecture: Designing and structuring of data systems, ensuring that data is stored, managed, and utilized efficiently

Data Processing Frameworks: Using tools and libraries to process large data sets efficiently, such as Apache Hadoop and Apache Spark.

Technical Documentation: Creating and maintaining documentation that explains the functionality, use, and maintenance of software or systems.

Deep Learning: Using a subset of machine learning involving neural networks with many layers, used to model complex patterns in data.

Statistical Analysis: Collecting and analyzing data to identify patterns and trends, and to make informed decisions.

Data Engineering: Designing and building systems for collecting, storing, and analyzing data at scale.

Definition of Proficiency Levels:

Foundational: This is the entry level of the skill, typically expected when starting a new role or working with the skill for the first time. You rely on strong manager support, coaching, and training as you build the capability to progress to higher proficiency levels.

Proficient: This is the level at which you are considered effective in the skill. You demonstrate more than just functional competence-you begin to have a noticeable impact in your role by applying the skill consistently and meaningfully. You require only minimal support, coaching, or training to apply the skill successfully.

Advanced: This is the level where you move beyond meeting expectations to actively leading, influencing, and delivering considerable impact across the wider business. You are seen as a role model, demonstrate the skill independently, and require little to no manager support.