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.

Role Summary

 

We are looking for an Associate AI/ML Engineer to accelerate our transition toward AI-driven, spec-first integration engineering. This role sits at the intersection of AI engineering, API integrations, and customer delivery, with a strong focus on building agentic workflows, MCP-enabled systems, and intelligent mapping engines to reduce time-to-integration and improve delivery outcomes.

 

Key Responsibilities

• Build and enhance AI-driven integration capabilities (e.g., schema mapping, transformation, validation) using probabilistic and ML-based approaches
• Design and implement agentic workflows (MCP-based tool orchestration, agent SDKs) for real-world integration use cases
• Develop and optimize retrieval and scoring mechanisms (e.g., lexical matching, similarity algorithms such as Jaccard) to improve automation accuracy
• Contribute to spec-driven development pipelines, enabling faster API onboarding and customer integration
• Work closely with delivery teams to solve real customer problems through AI-assisted integration patterns
• Build lightweight UI/UX interfaces to visualize mappings, validation outputs, and agent workflows
• Support Java/Python-based services for integration orchestration and backend processing
• Ensure operational excellence through testing, validation, monitoring, and continuous improvement of AI workflows

Core Skills & Experience

• Strong foundation in Python (primary) with exposure to Java-based services
• Understanding of machine learning / probabilistic algorithms (e.g., similarity scoring, ranking, heuristics)
• Familiarity with LLM integration patterns (RAG, prompt design, context handling)
• Exposure to agentic systems (MCP, tool orchestration, workflow automation)
• Experience or interest in API integrations, schema mapping, and data transformation
• Basic understanding of frontend/UI development (for internal tools / visualization)
• Strong problem-solving mindset with ability to work on real-world, ambiguous customer use cases

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  [email protected]. 

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.