Related skills
rag llm dynamic memory management memory_architectures📋 Description
- Design and build a Dynamic Memory Management system for LLM/agent apps.
- Develop RAG and memory architectures for context-aware reasoning.
- Create ranking and retrieval strategies for memory grounding.
- Build pipelines for memory selection and grounding in context.
- Train and post-train models for reliable function calling.
- Lead cross-functional efforts to ship memory capabilities end-to-end.
🎯 Requirements
- Proven track record deploying LLM-powered agent systems in production.
- Strong RAG, embedding, retrieval, and memory architecture expertise.
- Deep understanding of memory in LLMs: short/long-term, personalization.
- Hands-on experience with function calling and tool calling in LLMs.
- Experience with fine-tuning and post-fine-tuning (RL-style) methods.
- Familiarity with discriminator/verifier models and evaluation for grounding.
🎁 Benefits
- Annual base compensation €75,000–€130,000 EUR.
- Comprehensive medical coverage for you and family.
- Unlimited PTO and 12 weeks parental leave.
- 401(k) with matching and ESOP plan.
- Life at Upwork: remote-first culture.
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