T3 AI/ML Engineer

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 Overview:

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.

Key Responsibilities:

Model Development & Innovation

• 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

Engineering Excellence & Quality

• 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

Data Solutions & Performance Optimization

• 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

Collaboration & Documentation

• 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

Required Qualifications

• 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

Preferred Qualifications

• 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

What You Will Bring

• 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

What Success Looks Like

• 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

Core Competencies

• 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  [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: 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.