T3 AIML 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.

TheData AI/ML Engineerdesigns 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 lifecyclefrom data preparation and model development to testing deployment monitoring and continuous improvementbuilding 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 implementrobust scalable and optimized machine learning and deep learning models with the ability to iterate quickly

• Research and implementnew models technologies and methodologies and integrate them into production systems with a focus on scalability and reliability

• Applycreative problem-solvingto design innovative tools develop algorithms and build optimized workflows

• Identify and implement theright data-driven approachesto solve ambiguous and open-ended business problems leveraging strong data engineering capabilities

• Write and integrateautomated testsalongside models and code to ensure reproducibility scalability and alignment with established quality standards

• Implement best practices insecurity pipeline automation and error handlingusing modern programming and data manipulation tools

• Understand and use the teamstechnical tools and frameworks including programming languages libraries and platforms

• Actively supportdebugging and refining codeacross AI/ML projects

• Independentlymanage and optimize data solutionsfor training inference and analytics use cases

• PerformA/B testing evaluate model and system performance and use results to drive continuous improvement

• Build and maintaindata pipelinesthat transform raw data into high-quality inputs for AI/ML systems

• Collaborate across teams to develop and implementhigh-quality scalable AI/ML solutionsaligned with business goals user needs and performance expectations

• Contribute to thedesign and documentationof 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

• Bachelors degree inComputer Science Data Science Statistics Mathematics Engineering or a related field (or equivalent experience)

• 5 yearsof experience in AI/ML engineering data science or related roles

• Strong programming skills inPython( Scala R)

• Hands-on experience withmachine learning and deep learning frameworkssuch as scikit-learn TensorFlow PyTorch or XGBoost

• Solid understanding ofstatistics model evaluation and experimentation(A/B testing cross-validation performance metrics)

• Experience withSQL data wrangling and working with large datasets

• Experience buildingautomated data/ML pipelinesusing tools such as Spark Airflow Kafka or cloud-native services

• Familiarity withtesting practicesfor ML systems (unit tests integration tests data validation reproducibility checks)

• Experience deploying models incloud or production environments(Azure AWS or GCP)

• Strong problem-solving skills and ability to work onambiguous open-ended business problems

• Masters degree in AI ML Data Science or a quantitative discipline

• Experience withMLOpstools and practices (MLflow Kubeflow SageMaker Azure ML Databricks etc.)

• Experience withLLMs NLP computer vision or advanced deep learning applications

• Knowledge ofsecurity best practicesfor AI/ML systems and sensitive data handling

• Experience withcontainerization and orchestration(Docker Kubernetes)

• Background in enterprise domains such asfinance logistics healthcare or e-commerce

• Experience withmodel monitoring drift detection and retraining strategies

• A mindset ofspeed and iterationwithout compromising quality

• Ability to balanceresearch and experimentationwithproduction readiness

• Strong ownership of end-to-end AI/ML solutions from problem framing to deployment

• A collaborative approach to building systems that arescalable reliable and business-aligned

• Models and pipelines areproduction-ready tested and reproducible

• AI/ML solutions solve real business problems and improve automation and decision-making

• Systems aresecure 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.

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CORE SKILLSProgramming: Writing code to manipulate analyze and visualize data often using languages like Python R and Level: ProficientAI & 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 dataProficiency Level: ProficientData Analysis: Inspecting cleansing transforming and modeling data to discover useful information draw conclusions and support decision-makingProficiency Level: FoundationalMachine Learning Pipelines: Using automated workflows that manage the end-to-end process of training and deploying machine learning Level: ProficientModel Deployment: Making a trained machine learning model available for use in production Level: ProficientSPECIALIZED SKILLSBig Data Technologies: Using continuous integration and continuous delivery (CI/CD) pipelines to automate the process of software development including building testing and deploying codeNatural Language Processing (NLP): Focusing on the interaction between computers and humans through natural Architecture: Designing and structuring of data systems ensuring that data is stored managed and utilized efficientlyData Processing Frameworks: Using tools and libraries to process large data sets efficiently such as Apache Hadoop and Apache Documentation: Creating and maintaining documentation that explains the functionality use and maintenance of software or Learning: Using a subset of machine learning involving neural networks with many layers used to model complex patterns in Analysis: Collecting and analyzing data to identify patterns and trends and to make informed Engineering: Designing and building systems for collecting storing and analyzing data at 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 : This is the level at which you are considered effective in the skill. You demonstrate more than just functional competenceyou 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 : 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.