Platform Product Owner-AI Enablement & Vendor Experience

Job description

Develops AI Automation and Agents for ASSP About Us: Maersk is a global leader in integrated logistics and have been industry pioneers for over a century. Through innovation and transformation, we are redefining the boundaries of possibility, continuously setting new standards for efficiency, sustainability, and excellence. At Maersk, we believe in the power of diversity, collaboration, and continuous learning and we work hard to ensure that the people in our organization reflect and understand the customers we exist to serve. With over 100,000 employees across 130 countries, we work together to shape the future of global trade and logistics. Join us as we harness cutting-edge technologies and unlock opportunities on a global scale. Together, let's sail towards a brighter, more sustainable future with Maersk. Role Purpose: As Platform Product Owner-AI Enablement, you will drive the delivery of agentic AI across the platform — from identifying use cases and ensuring data foundations are in place, through to building, launching, and tracking the value they create. You will translate process pain points into well-scoped AI opportunities, ensure data is catalogued and production-ready before any agent is built, and coordinate with enterprise architects and engineering managers to bring agents into production. You will use tools such as Claude Code, Microsoft Copilot, and modern AI orchestration frameworks to prototype and build AI agents directly — operating as a hands-on builder, not only a specification writer. Core Accountabilities: 1. AI Use Case Development & Pipeline ·  Partner with use case owners and Global Process Leads to identify, qualify, and size AI opportunities across enterprise workflows. · Maintain a sequenced AI use case pipeline aligned to the AI Transformation roadmap and OP priorities. 2. Data & Catalogue Enablement · Define data requirements (sources, fields, quality thresholds) needed for each agent to function reliably, and own Data Catalogue entries as a mandatory gate before POC or build. · Build and maintain semantic search and retrieval layers (vector databases, embedding pipelines) to support agent grounding. 3. Technical Delivery & Hands-On AI Development · Co-own technical design of AI agents with architects and engineering managers; define APIs, data flows, and integration patterns. · Design and build RAG pipelines, multi-agent workflows, and orchestration logic using frameworks such as LangChain, LangGraph, LlamaIndex, or AutoGen. · Fine-tune prompts and evaluate outputs across LLMs (GPT, Claude, Gemini, or open-source models) for business scenarios. · Deploy AI solutions on cloud platforms (Azure, AWS, or GCP), applying responsible AI and governance practices throughout. 4. Adoption & Value Tracking · Own business readiness for each launch (stakeholder alignment, training, communication) and mitigate adoption risks early. · Define and track business KPIs (e.g. hours saved, error reduction) and technical KPIs (e.g. accuracy, latency) per agent, reporting value realisation to the Head of AI Enablement. Skills & Experience Required: ·Experience in product ownership, business analysis, or process improvement within a procurement, supply chain, or operations environment. ·Demonstrable hands-on daily use of AI tools (Claude, Copilot, ChatGPT or equivalent) to solve real work problems. · Working proficiency in Python and SQL, sufficient to prototype and iterate on agent code, not just specify it. · Comfortable reading datasets, writing basic SQL, and assessing quality without needing to be a data engineer. Preferred: ·Deeper agentic AI experience (multi-agent systems, tool use, prompt engineering) with at least a working prototype built. ·Experience with Databricks, Snowflake, Spark, or similar data platforms, and deploying AI workloads on major cloud providers. · Hands-on experience with at least one AI orchestration framework (LangChain, LangGraph, LlamaIndex, or AutoGen) and practical understanding of RAG, embeddings, and vector databases. ·Familiarity with data catalog/lineage concepts, MCP (Model Context Protocol), or transformation/platform product roles in a large matrixed organisation. ·Experience with procurement processes and platforms (e.g. Source-to-Contract, Procure-to-Pay, or vendor management systems). Maersk is committed to a diverse and inclusive workplace, and we embrace different styles of thinking. Maersk is a