A.P. Moller - Maersk
A.P. Moller – Maersk is the global leader in container shipping services. The business operates in 130 countries and employs 80,000 staff. An integrated container logistics company, Maersk aims to connect and simplify its customers’ supply chains.
Today, we have more than 180 nationalities represented in our workforce across 131 Countries and this mean, we have elevated level of responsibility to continue to build inclusive workforce that is truly representative of our customers and their customers and our vendor partners too.
We are responsible for moving 20 % of global trade & is on a mission to become the Global Integrator of Container Logistics. To achieve this, we are transforming into an industrial digital giant by combining our assets across air, land, ocean, and ports with our growing portfolio of digital assets to connect and simplify our customer’s supply chain through global end-to-end solutions, all the while rethinking the way we engage with customers and partners.
The Brief
As a Senior AI/ML Engineer in our Data & AI Governance team, you’ll build the systems that improve how Maersk detects, manages, and fixes data quality issues at scale while contributing to responsible AI observability and compliance tooling.
This is a hands-on engineering role focused on platform-level tooling for data reliability, model traceability, and metadata intelligence. You’ll work across structured and unstructured data, help enforce quality SLAs and contribute to components that support the governance of AI/ML models.
The role sits at the intersection of platform engineering, data operations, and applied AI - ideal for someone who enjoys building reusable tools, mentoring others, and making complex systems more reliable and auditable.
This is a key part of our long-term vision to treat data quality with the same urgency and rigor as platform reliability. The systems you build will help set a new standard for how we manage quality, fairness, and trust in enterprise data and AI.
Senior AI/ML Engineer
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 accommodationrequests@maersk.com.
What I'll be doing – your accountabilities?
- Build and scale AI/ML-driven components to detect data anomalies, schema drift, and degradation in real-time across pipelines
- Develop validation logic, auto-profiling tools, and scoring engines to assess and monitor enterprise data quality
- Design architecture for AI/ ML-based DQ solutions that are modular, reusable, and scalable
- Apply AI/ML techniques including NLP, rule induction, and pattern classification to enrich metadata and detect systemic quality issues
- Build tooling to support responsible AI: drift tracking, fairness detection, explainability indicators, and lifecycle logging
- Partner with platform engineers to integrate these tools into orchestration systems (e.g., Airflow, MLflow, or Dagster)
- Work with data owners and stewards to operationalize quality ownership using MIDAS – Maersk’s enterprise AI platform for metadata inventory, data accountability, and governance
- Contribute to the creation of a DataOps playbook with SLAs, page-zero metrics, escalation routines, and ownership models
- Mentor junior engineers and shape architectural and engineering best practices for AI/ML observability and data quality tooling
Foundational Skills
- Expert-level Python engineering experience with a proven ability to ship AI/ML-backed tooling at production scale
- Advanced knowledge of data pipelines and orchestration frameworks (e.g., Airflow, Spark, Dagster)
- Expert understanding of system observability - logging, telemetry, health scoring applied to data and model workflows
- Proven track record of applying advanced AI/ML techniques (e.g., classification, clustering, anomaly detection) in production settings
- Strong grounding in solution architecture for data-intensive, distributed systems
Specialized Skills
- Deep experience applying AI/ML to data quality use cases such as profiling, anomaly detection, drift analysis, and schema inference
- Expertise in metadata management, lineage tracing, and automated documentation (e.g., via DataHub, Unity Catalog, or Collibra)
- Hands-on experience with responsible AI tooling (e.g., SHAP, LIME, Fairlearn, What-If Tool) for explainability and bias detection
- Built or contributed to platform-level components that are used across domains, not just in isolated project delivery
- Ability to design and implement architectural patterns that support federated ownership, reuse, and lifecycle transparency
- 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
- 8+ years of engineering experience, including at least 3 years building and deploying AI/ML solutions in production
- Demonstrated experience building DQ and model observability tools - not just core predictive systems
- Strong experience working in cross-functional platform teams that deliver shared services used across business units
- Fluent in MLOps tooling (e.g., MLflow, SageMaker, Vertex AI) and capable of versioning, tracking, and documenting model behavior
- Strong communication and documentation skills; able to make complex system behavior understandable and operable
- Passion for enabling trustworthy AI through high-quality engineering practices
Preferred Experiences
In addition to basic qualifications, would be great if you have…
- Experience implementing data quality scoring, monitoring, or root cause tooling in a production environment
- Experience working with shared metadata systems and operationalizing lineage or traceability at scale
- Strong involvement in platform teams or developer enablement functions - not just analytics or research delivery
- Applied experience with model explainability, fairness evaluation, or lifecycle documentation tooling
- Understanding of enterprise AI risk and how to translate policy into engineering design constraints
