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About the company:
We are a trusted, family-founded company and global pioneers with a worldwide reputation for excellence in bulk-liquid and chemical logistics, transportation, and storage. Our passion for innovation drives us to lead the industry forward. Across our logistics, shipping, and seafood businesses, we deliver world-class reliability, flexibility, and expertise – ensuring that complex operations run seamlessly.
About the Role:
We are looking for an AI Engineer whose primary focus is designing and building agentic workflows that automate, refine, and optimize business processes end-to-end. Beyond agentic work, you will also contribute to developing data products and optimization models that drive business outcomes through better decision-making. You bring a background in software engineering and applied AI, with a systems mindset toward measurable business impact.
You'll be part of our Data & AI team, working closely with business stakeholders and the platform and data teams to identify automation and optimization opportunities, translate them into production-grade AI solutions, and continuously refine them based on performance and business feedback.
At Stolt-Nielsen, we take Digital seriously. We invest in our teams through training and mentoring and enable them with the required tools to work in a modern way (engineering laptops, Cursor licenses). As we are further building up our engineering practice, there is ample room for you to contribute, take initiative and shape the future of our ways of working and technology landscape.
What You'll Do:
What You'll Bring:
Must Have Skills:
Nice to Have Skills:
Job ID: 151829837
Skills:
Jax, Tensorflow, Pytorch, Gcp, Restful Apis, Azure, Python, AWS, LangChain, LLMs, Crew.ai, LangFuse, Go, LangSmith, LangGraph, RAG
Skills:
python, Sql, Problem Solving Skills, Stakeholder Management, agentic frameworks
Skills:
Tensorflow, Gcp, Pytorch, Azure, Python, AWS, LangChain, data pipelines, parameter tuning, model optimization, AI ML frameworks
Skills:
Python
Skills:
Jax, Microservices, Tensorflow, Deep Learning, Pytorch, Docker, Distributed Systems, Python, AWS, Machine Learning, Apis, Google Cloud, Azure, Kubernetes, embeddings, Hugging Face Transformers, vector databases, CI CD, Optimisation, model evaluation, probability statistics, large language models, cloud-native architectures, model monitoring tools, experiment tracking, Transformers, retrieval-augmented generation