Core Responsibilities
- Develop AI Agent Systems: Build intelligent search and task execution agents using ReAct, planning, and multi-agent frameworks (e.g., LangGraph, Dify, CrewAI)
- Optimize End-to-End RAG Pipelines: Build and refine efficient RAG systems from ingestion, chunking, and embedding to hybrid vector search (OpenSearch), implementing precise grounding and citation
- LLM Training & Alignment: Conduct advanced post-training (SFT, RLHF, continual pretraining) and align models for reliable JSON-schema function calling and external tool usage
- Automated Evaluation & Iteration: Build offline/online evaluation pipelines using synthetic QA, retrieval metrics, and hallucination detection to continuously improve system performance and stability
Qualifications
- Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related field
- 3+ years of experience developing AI systems, with a focus on RAG, Agent architectures, or LLM training/optimization
- Proficiency in Python and key ML frameworks (PyTorch/TensorFlow), with experience in distributed training and high-performance inference
- Hands-on, in-depth experience in at least two of the following domains:
- End-to-end RAG pipeline development and optimization with OpenSearch/vector databases
- AI Agent framework development (LangGraph, CrewAI, ReAct)
- Advanced LLM training (SFT, RLHF, LoRA) and alignment techniques
- Excellent problem-solving and systems thinking skills
- Passion for Web3 and AI is a plus
Key Outcomes
- Deliver a high-accuracy, low-latency AI Agent system to power intelligent Web3 applications
- Achieve continuous improvement in RAG retrieval accuracy and establish an automated evaluation and iteration loop
- Drive LLM performance optimization
