AIML Engineer (Data Engineering + AI Focus)

Data AI/ML (Artificial Intelligence and Machine Learning) Engineering involves the use of algorithms and statistical models to enable systems to analyze data learn patterns and make data-driven predictions or decisions without explicit human programming. AI/ML applications leverage vast amounts of data to identify insights automate processes and solve complex problems across a wide range of fields including healthcare finance e-commerce and more. AI/ML processes transform raw data into actionable intelligence enabling automation predictive analytics and intelligent solutions. Data AI/ML combines advanced statistical modeling computational power and data engineering to build intelligent systems that can learn adapt and automate decisions.

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 80000 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 Brief

In this role as an AI/ML Engineer within the Global Data & Analytics (GDA) team you will build and scale data-intensive AI/ML solutions that power intelligent decision-making across Maersks supply chain ecosystem.

This role combinesstrong data engineering foundations with applied AI/ML expertise enabling the development of robust data pipelines scalable ML systems and production-grade AI applications.

You will work on designing reliable data platforms enabling high-quality data availability and embedding advanced analytics and AI capabilities into business workflows.

Key Responsibilities:

• Partner with business product and engineering teams to define scalable data and AI/ML solutions aligned with measurable business outcomes.
• Design build and maintainrobust data pipelines and data platformsusing Python Spark/PySpark and cloud-native services.
• Develop and deployend-to-end AI/ML solutions including data ingestion feature engineering model training evaluation and production deployment.
• Architect and optimizelarge-scale data processing systemson cloud platforms (Azure/AWS) ensuring performance reliability and cost efficiency.
• Implement and manageMLOps and DataOps practices including CI/CD pipelines model versioning monitoring and automated retraining.
• Work withLLMs and Generative AI systems integrating them into production workflows using frameworks such as LangChain LlamaIndex or similar.
• Design and maintaindata models (batch and streaming)that support analytics reporting and AI use cases.
• Ensure data quality governance and observability across pipelines and ML systems.
• Optimize infrastructure and workloads forcost scalability and performance including efficient use of compute and storage.
• Collaborate with engineering teams to deploy solutions usingcontainerization and orchestration (Docker Kubernetes).
• Mentor junior engineers and contribute to engineering excellence through code reviews and best practices.

We are looking for:

• 7 years of experience acrossData Engineering and AI/ML engineeringin production environments.
• Strong programming skills inPython and SQL with hands-on expertise inSpark/PySparkfor large-scale data processing.
• Deep experience withcloud platforms (Azure or AWS) including services for data engineering ML and distributed systems (e.g. Databricks Synapse EMR S3 ADF etc.).
• Hands-on experience in building and deployingscalable data pipelines and ML systems.
• Strong understanding ofdata modeling (data lakes lakehouse warehouse medallion architecture).
• Experience withMLOps frameworks(e.g. MLflow) and production model lifecycle management.
• Practical experience withLLMs / Generative AI applications including RAG document processing or workflow automation.
• Experience withcontainerization (Docker) and orchestration (Kubernetes).
• Strong understanding ofsystem design scalability and cost optimizationin cloud environments.
• Experience withdata quality observability and monitoring frameworks.
• Ability to translate business problems into scalable technical solutions with measurable impact.
• Prior experience in logistics supply chain or operations is a plus.
• Experience in simulation optimization or operations research is an added advantage.

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 .

CORE SKILLSProgramming: Writing code to manipulate analyze and visualize data often using languages like Python R and Level: ProficientAI & 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 dataProficiency Level: ProficientData Analysis: Inspecting cleansing transforming and modeling data to discover useful information draw conclusions and support decision-makingProficiency Level: FoundationalMachine Learning Pipelines: Using automated workflows that manage the end-to-end process of training and deploying machine learning Level: ProficientModel Deployment: Making a trained machine learning model available for use in production Level: ProficientSPECIALIZED SKILLSBig Data Technologies: Using continuous integration and continuous delivery (CI/CD) pipelines to automate the process of software development including building testing and deploying codeNatural Language Processing (NLP): Focusing on the interaction between computers and humans through natural Architecture: Designing and structuring of data systems ensuring that data is stored managed and utilized efficientlyData Processing Frameworks: Using tools and libraries to process large data sets efficiently such as Apache Hadoop and Apache Documentation: Creating and maintaining documentation that explains the functionality use and maintenance of software or Learning: Using a subset of machine learning involving neural networks with many layers used to model complex patterns in Analysis: Collecting and analyzing data to identify patterns and trends and to make informed Engineering: Designing and building systems for collecting storing and analyzing data at 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 : This is the level at which you are considered effective in the skill. You demonstrate more than just functional competenceyou 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 : 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.