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Description
We are looking for a strong AI / Data Engineer to join our EMEIA Supply Chain team. This role focuses on building AI-powered applications, scalable backend systems, and Generative AI solutions that solve real operational problems across supply chain and logistics.

You will work across modern AI engineering, prompt engineering, and intelligent application development, helping design the next generation of AI tools and data capabilities used by Operations teams across EMEIA.

If you are passionate about AI, data platforms, and solving complex supply chain challenges at scale, we’d love to hear from you.
","responsibilities":"Develop AI-powered applications and workflows leveraging Large Language Models (LLMs), prompt engineering, RAG architectures, and agent-based AI solutions.
Design and build agentic AI capabilities including multi-agent workflows, orchestration patterns, tool integrations, memory/context handling, and intelligent automation solutions for Supply Chain and Operations use cases.
Build and optimise backend AI and data services using technologies such as Snowflake, Airflow, Python, and related cloud tooling.
Develop and refine prompts, system instructions, context management strategies, and AI evaluation methodologies to improve the quality, reliability, and consistency of AI-generated outputs.
Work closely with Supply Chain and Operations teams to understand business challenges and deliver scalable AI and analytics solutions.
Support the deployment and operationalisation of AI solutions including monitoring, testing, governance, and continuous improvement.
Contribute to team-wide best practices around AI engineering, data engineering, and modern software development.
Build APIs, tools, and lightweight applications that enable business users to consume data and AI capabilities more effectively.

Preferred Qualifications
Experience building AI evaluation platforms covering hallucination detection, retrieval quality evaluation, groundedness scoring, prompt benchmarking, and model comparison testing.
Experience designing enterprise AI solutions using MCP architectures, knowledge graphs, or semantic data products.
Demonstrated interest in emerging AI technologies, evidenced through applied projects, research, or professional experience within enterprise or operational environments.
BSc or equivalent experience in Computer Science, Software Engineering, Data Engineering, Data Science, Artificial Intelligence, Mathematics, Statistics, or a related technical field.
MSc or equivalent advanced experience in AI, Machine Learning, Data Engineering, or a quantitative discipline is preferred but not required.
Certifications in cloud platforms, AI engineering, or Generative AI technologies.

Minimum Qualifications
Progressive experience in data engineering, AI engineering, machine learning infrastructure, or a related technical field.
Strong programming skills in Python and SQL, with experience building scalable ETL/ELT pipelines and working with large-scale datasets.
Hands on experience with modern data platforms and distributed processing technologies such as Snowflake, Spark, Kafka, Airflow, dbt, or equivalent technologies.
Experience working with Large Language Models (LLMs), including prompt engineering, agentic AI workflows, and AI application development.
Exposure to application frameworks such as FastAPI, Flask, Streamlit, or React.
Strong understanding of data architecture, APIs, and scalable system design.
Ability to communicate technical solutions clearly to both technical and non-technical audiences.