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.
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
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 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 .
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: ProficientMachine Learning Pipelines: Using automated workflows that manage the end-to-end process of training and deploying machine learning Level: AdvancedModel Deployment: Making a trained machine learning model available for use in production Level: AdvancedSPECIALIZED 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.
Required Experience:
Senior IC
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 d...
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.
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
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 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 .
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: ProficientMachine Learning Pipelines: Using automated workflows that manage the end-to-end process of training and deploying machine learning Level: AdvancedModel Deployment: Making a trained machine learning model available for use in production Level: AdvancedSPECIALIZED 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.
Required Experience:
Senior IC
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8 hours ago
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