Nlp / Llm 应用工程师
Tasks
- Balance model quality and cost
- Build agent model capability layer
- Build embedding pipeline
- Build golden dataset
- Create knowledge question answering
- Define evaluation metrics
- Design intent recognition
- Design prompt engineering framework
- Develop agent evaluation system
- Develop semantic search for enterprise knowledge base
- Implement RAG retrieval augmented generation
- Implement automated evaluation pipeline
- Implement dialogue management
- Implement multi model routing strategy
- Optimize context engineering
Perks/Benefits
- N/A
Skills/Tech-stack
Anthropic API | Chroma | Embedding | Genspark | Genspark RAG Architecture | Genspark Search Agent | Langchain | Langgraph | Llamaindex | Manus | Milvus | OpenAI API | Pinecone | Prompt engineering | Python | RAG Architecture | RAG evaluation | Retrieval-Augmented Generation | Vector Database | Vector Search
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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