Software Engineer

About Maersk Maersk is building the next generation of digital capabilities for the global supply chain.

We are re-architecting our platform with a data-first approach — building a trusted, scalable and reusable data foundation for current and future supply-chain use cases.

On top of this foundation, we will build ML models, intelligence, recommendations, GenAI and agentic AI to help customers and teams make better and faster decisions.

A key part of this vision is data democratization — making trusted and contextualised data easy to discover, understand and use across teams, products, models and AI agents.

About The Role We are looking for an experienced Data, AI/ML, GenAI & Agentic AI Engineer to help design and build this platform from the ground up.

This role combines data engineering, software engineering, AI/ML and architecture. You will build reusable platform capabilities rather than one-off solutions and work closely with engineers, data scientists, architects, product teams and supply-chain experts.

What You Will Do

• Design and build scalable data and AI architecture for data-intensive systems.
• Build trusted data foundations and reusable data products from multiple sources.
• Build reliable batch, streaming and near-real-time data pipelines based on business needs.
• Enable data democratization and self-service access to trusted data.
• Build capabilities for data quality, governance, lineage, metadata, security and observability.
• Establish common business context and semantics so data can be effectively used by applications, ML models, GenAI and AI agents.
• Develop and productionise ML solutions for prediction, forecasting, risk, anomaly detection, recommendations and optimisation

Build production-grade GenAI/LLM solutions using approaches such as RAG, embeddings, vector search and tool calling.

• Build agentic AI solutions that can reason over trusted data, use tools and APIs, provide recommendations and take actions.
• Design appropriate guardrails, access controls, monitoring and human oversight for AI and agentic solutions.
• Build reusable APIs, services and workflows that connect data, applications and AI capabilities.
• Use platforms such as Pipedream or similar workflow tools where they add value.
• Make architecture and technology decisions considering scalability, reliability, security, performance and cost.
• Establish strong engineering practices including testing, CI/CD and observability.
• Contribute to architecture reviews, technical design and code reviews.
• Mentor engineers and help establish engineering and architecture standards

About You We are looking for someone with strong engineering, data and AI fundamentals who can build production-ready systems.

You should have:

• 4+ years of experience in software engineering, data engineering, AI/ML engineering or a related field.
• Experience designing and building scalable, distributed and data-intensive systems.
• Experience contributing to or owning architecture and technical design for data or AI platforms.
• Strong programming skills in Java and Python. Experience with Scala, Kotlin or similar languages is a plus.
• Strong understanding of data architecture, data modelling, distributed systems and data processing.
• Experience building data pipelines, data products or data platforms at scale.
• Experience with technologies such as Kafka, Spark, Databricks, Flink or equivalent.
• Experience with relational and/or NoSQL databases
• Good understanding of data quality, governance, metadata, lineage and observability.
• Hands-on experience taking ML models from development to production, including deployment, monitoring and evaluation.
• Experience with GenAI/LLMs and building practical, production-ready applications.
• Understanding of RAG, embeddings, vector search, tool calling and LLM evaluation.
• Experience or strong exposure to AI agents and agentic workflows.
• Understanding of AI security, guardrails, access control and human oversight.
• Experience with at least one cloud platform such as Azure, AWS or Google Cloud.
• Strong software engineering practices including automated testing, CI/CD and production operations.
• Strong communication and problem-solving skills.
• A platform mindset — focused on building reusable capabilities rather than oneoff solutions.

Good to Have

• Experience in logistics, shipping, transportation or supply chain.
• Ex…