AI/ML 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 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 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 Maersk’s supply chain ecosystem. This role combines strong 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 maintain robust data pipelines and data platforms using Python, Spark/PySpark, and cloud-native services. Develop and deploy end-to-end AI/ML solutions, including data ingestion, feature engineering, model training, evaluation, and production deployment. Architect and optimize large-scale data processing systems on cloud platforms (Azure/AWS), ensuring performance, reliability, and cost efficiency. Implement and manage MLOps and DataOps practices, including CI/CD pipelines, model versioning, monitoring, and automated retraining. Work with LLMs and Generative AI systems, integrating them into production workflows using frameworks such as LangChain, LlamaIndex, or similar. Design and maintain data 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 for cost, scalability, and performance, including efficient use of compute and storage. Collaborate with engineering teams to deploy solutions using containerization 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 across Data Engineering and AI/ML engineering in production environments. Strong programming skills in Python and SQL, with hands-on expertise in Spark/PySpark for large-scale data processing. Deep experience with cloud 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 deploying scalable data pipelines and ML systems. Strong understanding of data modeling (data lakes, lakehouse, warehouse, medallion architecture). Experience with MLOps frameworks (e.g., MLflow) and production model lifecycle management. Practical experience with LLMs / Generative AI applications, including RAG, document processing, or workflow automation. Experience with containerization (Docker) and orchestration (Kubernetes). Strong understanding of system design, scalability, and cost optimization in cloud environments. Experience with data 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 accommodationrequests@maersk.com . CORE SKILLS 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.