Senior AI Engineering

About the Role
Help turn AI ideas into systems people can rely on. An AI application needs more than a convincing first answer; it requires relevant data, useful tools, measurable quality, and reliable software behind it. As a Working Student in AI Engineering, you will have the opportunity to develop practical applications using large language models while working with a team of experienced engineers.

What You’ll Work On
You will:

• Help develop Python applications that connect large language models to data, APIs, and tools.
• Build and improve retrieval-augmented generation (RAG) workflows, including document processing, chunking, embeddings, and retrieval.
• Support document intelligence pipelines that extract structured, source-linked information from complex documents.
• Implement and test agent workflows, including tool use and human approval steps.
• Create evaluation datasets and compare answer quality, retrieval performance, latency, and cost.
• Add tests, logging, and documentation to make AI applications easier to operate and improve.

What You Bring

• Practical Python skills with APIs, JSON, and Git.
• A good understanding of large language models and curiosity about how AI applications are built.
• A careful approach to testing: you understand when a system works, when it fails, and why.
• Good English communication skills.
• A university or personal project involving LLMs, RAG, embeddings, or AI agents; experience with Amazon Bedrock, Claude, vector databases, FastAPI, Docker, or an AI application framework is a plus.

What You Can Expect

• Exposure to the full application lifecycle, from experimentation and evaluation to deployment and monitoring.
• A small team where your questions, ideas, and contributions matter.

We welcome applicants of all backgrounds. You do not need to meet every listed requirement to apply.