The Data AI/ML Engineer designs, builds, and deploys intelligent systems that transform raw data into actionable insights. This role applies machine learning, deep learning, statistical modeling, and data engineering to help systems learn patterns, automate decisions, and solve complex business problems without explicit manual programming.
You will work across the full AI/ML lifecycle—from data preparation and model development to testing, deployment, monitoring, and continuous improvement—building scalable, secure, and production-ready solutions that deliver measurable business impact in areas such as automation, predictive analytics, and intelligent decision-making.
Design, develop, and implement robust, scalable, and optimized machine learning and deep learning models, with the ability to iterate quickly
Research and implement new models, technologies, and methodologies, and integrate them into production systems with a focus on scalability and reliability
Apply creative problem-solving to design innovative tools, develop algorithms, and build optimized workflows
Identify and implement the right data-driven approaches to solve ambiguous and open-ended business problems, leveraging strong data engineering capabilities
Write and integrate automated tests alongside models and code to ensure reproducibility, scalability, and alignment with established quality standards
Implement best practices in security, pipeline automation, and error handling using modern programming and data manipulation tools
Understand and use the team’s technical tools and frameworks, including programming languages, libraries, and platforms
Actively support debugging and refining code across AI/ML projects
Independently manage and optimize data solutions for training, inference, and analytics use cases
Perform A/B testing, evaluate model and system performance, and use results to drive continuous improvement
Build and maintain data pipelines that transform raw data into high-quality inputs for AI/ML systems
Collaborate across teams to develop and implement high-quality, scalable AI/ML solutions aligned with business goals, user needs, and performance expectations
Contribute to the design and documentation of AI/ML solutions, clearly detailing methodologies, assumptions, limitations, and findings for future reference and cross-team collaboration
Communicate technical concepts effectively to both technical and non-technical stakeholders
Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field (or equivalent experience)
5+ years of experience in AI/ML engineering, data science, or related roles
Strong programming skills in Python ( Scala, R)
Hands-on experience with machine learning and deep learning frameworks such as scikit-learn, TensorFlow, PyTorch, or XGBoost
Solid understanding of statistics, model evaluation, and experimentation (A/B testing, cross-validation, performance metrics)
Experience with SQL, data wrangling, and working with large datasets
Experience building automated data/ML pipelines using tools such as Spark, Airflow, Kafka, or cloud-native services
Familiarity with testing practices for ML systems (unit tests, integration tests, data validation, reproducibility checks)
Experience deploying models in cloud or production environments (Azure, AWS, or GCP)
Strong problem-solving skills and ability to work on ambiguous, open-ended business problems
Master’s degree in AI, ML, Data Science, or a quantitative discipline
Experience with MLOps tools and practices (MLflow, Kubeflow, SageMaker, Azure ML, Databricks, etc.)
Experience with LLMs, NLP, computer vision, or advanced deep learning applications
Knowledge of security best practices for AI/ML systems and sensitive data handling
Experience with containerization and orchestration (Docker, Kubernetes)
Background in enterprise domains such as finance, logistics, healthcare, or e-commerce
Experience with model monitoring, drift detection, and retraining strategies
A mindset of speed and iteration without compromising quality
Ability to balance research and experimentation with production readiness
Strong ownership of end-to-end AI/ML solutions, from problem framing to deployment
A collaborative approach to building systems that are scalable, reliable, and business-aligned
Models and pipelines are production-ready, tested, and reproducible
AI/ML solutions solve real business problems and improve automation and decision-making
Systems are secure, scalable, and well-documented
Performance is measured, evaluated, and continuously improved through experimentation
Cross-functional teams can understand, maintain, and extend AI/ML solutions over time
Machine learning & deep learning model development
Data engineering & pipeline automation
Automated testing & quality assurance for ML systems
Security, error handling, and operational best practices
A/B testing & performance evaluation
Creative problem-solving & algorithm design
Cross-team collaboration & technical documentation
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.