Senior AI Platform Engineer

About the Company

Responsibilities

• ● A.P. Moller - Maersk
• ● A.P. Moller - Maersk is the global leader in container shipping services. The business operates in 130 countries and employs c. 80,000 staff. An integrated container logistics company, Maersk aims to connect and simplify its customers' supply chains.
• ● AI/ML Platform Engineering focuses on building scalable engineering platforms, infrastructure, automation, and operational capabilities that enable AI and Machine Learning teams to develop, deploy, integrate, monitor, and operate AI/ML solutions efficiently and securely. AI/ML platforms provide standardized tools and services for model lifecycle management, MLOps, model serving, Generative AI, LLM applications, agentic AI, experimentation, deployment, observability, and governance. AI/ML Platform Engineering combines cloud infrastructure, software engineering, DevOps, MLOps, Kubernetes, automation, and AI technologies to create reliable and self-service platforms that accelerate enterprise AI adoption.
• ● 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
• ● AI & Machine Learning: Creating AI-powered solutions using Generative AI, Agentic AI and machine learning technologies to solve business problems and automate processes.
• ● Proficiency Level: Proficient
• ● Cloud Platform Engineering: Designing, deploying and operating secure, scalable and highly available cloud-native platforms on AWS.
• ● Proficiency Level: Advanced
• ● Programming: Writing production-grade applications, platform services and automation using languages such as Python, Java and SQL.
• ● Proficiency Level: Advanced