Associate AIML Engineer(GenAI)

Associate AI/ML Engineer (GenAI)

A.P. Moller - Maersk
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 a shared passion for harnessing data, Generative AI, data science (DS), machine learning (ML), advanced simulation, optimization, and engineering excellence to create meaningful impact for our customers and operations worldwide.

We are a team, not a collection of individuals. We value our diverse backgrounds, perspectives, and strengths. We foster trust, constructive challenge, and thoughtful debate. We hold one another accountable and cultivate a strong feedback culture that supports both professional growth and personal development.

We are now seeking a Generative AI Scientist who is excited about designing and building intelligent AI systems, including LLM-powered applications, agentic frameworks, and advanced ML solutions, that enhance operational intelligence, automate complex workflows, and generate actionable insights for container terminals globally, helping optimize yard flows, vessel operations, and decision-making to drive efficiency and measurable business value.

We Offer – This Is What You Get:

You will be part of the APM Terminals team within Global Data & Analytics (GDA), responsible for developing and delivering enterprise-grade Generative AI systems that power intelligent decision-making across container shipping terminals. As a Generative AI Scientist, you will play a leading role in designing, building, scaling, and continuously improving AI products that directly impact terminal operations and operational efficiency.

This position offers a unique opportunity to apply your expertise in large language models, Retrieval-Augmented Generation (RAG), agentic AI frameworks, semantic search, and modern AI engineering practices to create solutions that automate complex workflows, enhance operational intelligence, and generate actionable insights for global terminal operations.

This is an exciting time to join a growing and dynamic team tackling some of the most complex challenges in terminal operations and shaping the future of AI-enabled global trade infrastructure. We place strong focus on our people, and the right candidate will have broad opportunities to deepen their capabilities in AI system architecture, evaluation science, production deployment, and scalable AI platform design within an environment defined by innovation, collaboration, and continuous progress.

Key Responsibilities

Generative AI Solution Development

• Design, implement, and deploy production-grade Generative AI solutions that enhance operational intelligence and automation across terminal workflows.
• Develop LLM-powered systems including retrieval-based reasoning, AI copilots, and task-oriented AI agents that support decision-making in complex operational environments.
• Design robust data pipelines that enable reliable context retrieval, semantic understanding, and grounded response generation.

AI System Engineering & Reliability

• Build and maintain end-to-end GenAI solution lifecycles, from data preparation and experimentation through deployment, monitoring, and iteration.
• Implement structured evaluation frameworks to measure output quality, reliability, latency, cost efficiency, and hallucination risk.
• Enhance system robustness through prompt design, tool integration, validation layers, and guardrail mechanisms.
• Contribute to production readiness through testing, observability, and performance optimization practices.

Collaboration & Business Alignment

• Work closely with stakeholders to translate operational challenges into structured AI problem statements with measurable success criteria.
• Communicate model behaviour, limitations, and trade-offs clearly to technical and non-technical audiences.
• Own delivery of solutions within defined architectural patterns and engineering standards.

To do this job, we imagine you have:

2+ years of industry experience building and deploying production-grade AI/ML systems, with strong hands-on experience in Generative AI systems.

PhD or M.Sc. in Machine Learning, Computer Science, Applied Mathematics, Statistics, Engineering, or related quantitative discipline (or equivalent practical experience).

Strong Generative AI Foundation

You have practical experience working with:

• Large Language Models (LLMs) and Transformer architectures
• Retrieval-Augmented Generation (RAG) systems
• Embedding-based semantic search
• Prompt engineering and structured reasoning workflows
• AI agents or tool-integrated LLM systems

You understand how to evaluate model outputs for reliability, faithfulness, performance, and cost efficiency in real-world environments.

Strong Software Engineering Foundation

You are a programmer first with a proven track record of writing production-grade code.You possess deep proficiency in Python and standard software engineering practices including:

• Object-Oriented Programming
• Design patterns
• Unit testing and validation
• Version control
• Maintainable and scalable system design

Deployment & Operational Mindset

Experience working with:

• Cloud environments
• CI/CD pipelines
• Containerization (Docker)
• Monitoring and performance tracking

You are comfortable delivering solutions in fast-paced, agile environments.

Nice to Have

• Experience with vector databases and semantic indexing platforms
• Exposure to AI observability and governance practices
• Domain experience in logistics, scheduling, or operational optimization environments
• Experience integrating GenAI systems with optimization or simulation models

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: Foundational 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: Foundational 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.