AIML Engineer

A.P. Moller - Maersk is an integrated container logistics company that is responsible for moving 20% of global trade every year. With a dedicated team of over 100000 employees across 130 countries we go all the way to connect and simplify global trade and help our customers grow and thrive. Maersks vision is to be the global integrator of logistics connecting and simplifying customers supply chains.

As an AI/ML Engineer in our Data & AI Governance team you will help build and maintain AI/ML-driven capabilities that improve how Maersk detects manages and resolves data quality issues while supporting responsible AI observability and governance. This is a hands-on engineering role focused on developing and operating solutions for data reliability metadata intelligence and AI lifecycle monitoring. You will work alongside senior engineers architects and product teams to build scalable capabilities that improve trust quality and transparency across enterprise data and AI platforms. The role sits at the intersection of data engineering AI/ML and governance making it ideal for someone who enjoys solving practical engineering challenges while contributing to the reliability and responsible use of data and AI.

What Ill be doing your accountabilities

• Build and maintain AI/ML-driven components to detect data anomalies schema drift and degradation across data pipelines
• Develop validation rules profiling capabilities and scoring mechanisms for enterprise data quality monitoring
• Apply AI/ML techniques such as classification anomaly detection and NLP to support metadata enrichment and quality analysis
• Support implementation of responsible AI capabilities including model monitoring explainability and lifecycle logging
• Partner with platform engineers to integrate solutions into orchestration and MLOps platforms
• Work with data owners and stewards to operationalize data quality ownership through MIDAS Maersks enterprise AI platform for metadata inventory data accountability and governance
• Support DataOps processes quality standards and operational excellence initiatives
• Participate in troubleshooting root cause analysis and continuous improvement activities
• Contribute to engineering documentation testing and deployment practices
• Collaborate with cross-functional teams to deliver scalable and reliable AI and data quality solutions

Foundational Skills :

• Strong Python programming skills
• Understanding of machine learning concepts including anomaly detection classification clustering and predictive analytics
• Knowledge of data engineering fundamentals including data pipelines data quality data observability and metadata management
• Experience working with structured and semi-structured data at scale
• Understanding of cloud platforms and modern data architectures
• Knowledge of software engineering best practices including testing CI/CD version control and code reviews
• Familiarity with monitoring observability and operational health metrics
• Strong analytical problem-solving and troubleshooting skills
• Ability to work effectively in Agile and cross-functional teams

Specialized Skills :

• Experience applying AI/ML techniques to data quality anomaly detection drift detection profiling or metadata enrichment
• Familiarity with Generative AI LLMs prompt engineering embeddings vector databases and RAG architectures
• Understanding of AI observability model monitoring and explainability concepts
• Familiarity with metadata platforms lineage data catalogues or governance tooling
• Exposure to DataOps MLOps and enterprise AI lifecycle management
• Eagerness to learn and contribute to AI governance frameworks (e.g. EU AI Act ISO 42001 NIST AI RMF) and translate those into engineering patterns

Qualifications & Requirements :

• Bachelors or Masters degree in Computer Science Data Science Engineering Mathematics or a related field
• 4-5 years of experience in Software Engineering Data Engineering AI Engineering Machine Learning or related disciplines
• Experience building deploying or supporting AI ML data or software solutions in production or enterprise environments
• Familiarity with machine learning frameworks and AI development toolchains
• Exposure to cloud-native data and AI services
• Experience working within Agile product or engineering teams
• Strong communication and stakeholder collaboration skills
• Ability to learn quickly adapt to new technologies and contribute in a fast-paced environment
• Passion for building reliable scalable and trustworthy AI solutions

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