Senior AI/ML Engineer

At Maersk, we are redefining global logistics through data, platform engineering, and AI-driven innovation. As part of this journey, we are building scalable platforms and intelligent systems that enable faster decision-making, operational efficiency, and seamless integration across the enterprise. 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.

Key Responsibilities :

• Independently design and implement scalable machine learning solutions and data systems, ensuring end to end workflows, large scale analytics and reliability
• Collaborate with stakeholders to translate business needs into data engineering solutions, evaluate user journeys and challenge business requirements to ensure seamless, value driven delivery and integration of solutions
• Implement and refine feature engineering, monitoring, ML pipelines, deploy models in production, and address challenges in data pipelines
• Apply innovative problem-solving techniques, leveraging advanced methodologies to find unique approaches to complex problems and improve outcomes
• Investigate and resolve complex challenges in data models and deployment to ensure reliable solutions that meet performance benchmarks
• Mentor team members through code reviews, pairing sessions, knowledge-sharing sessions, and contribute to Communities of Practice
• Communicate technology, infrastructure, and deployment decisions clearly to both technical and non-technical stakeholders while maintaining detailed documentation to ensure reproducibility, scalability, and understanding
• Ensure readiness for production releases, focusing on testing, monitoring, observability, and maintaining scalability and reusability of models for future projects
• Drive cross-team and cross-discipline initiatives to optimize workflows, remove redundant applications and processes, share best practices, and enhance collaboration between teams
• Demonstrate awareness of shared platform capabilities and actively identify opportunities to leverage them in designing efficient and scalable data engineering solutions

What are we looking for?

Core Skills :

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

• 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

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

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

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

Specialised 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.

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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