Related skills
python kubernetes llms rag vector databases📋 Description
- Design and build LLM-powered product features used in production.
- Develop agentic workflows coordinating multiple AI components.
- Implement RAG architectures using embeddings and vector search.
- Build systems for prompting, context engineering, and tool usage.
- Develop evaluation frameworks to measure LLM and agent performance.
- Work with product/platform teams to turn AI into reliable features.
🎯 Requirements
- Strong Python programming for production-grade code.
- Hands-on experience with LLMs (training, finetuning).
- Building/operating LLM inference services in production.
- Experience with embeddings, vector databases, semantic search.
- Practical experience implementing RAG architectures.
- Designing robust evaluations for agent workflows and generative systems.
🎁 Benefits
- Hybrid work in Copenhagen office.
- Equipment provided by Corti.
- Full-time role starting as soon as possible.
- Make an impact on global healthcare.
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