Job Description

Job IntroductionWe are building the most advanced AI Agent for the Web3 industry, leveraging the largest proprietary dataset in the field. We seek a core algorithm engineer to architect AI Agent systems, optimize end-to-end RAG pipelines, implement LLM training/alignment, and deploy scalable.
Core Responsibilities1. Develop AI Agent Systems: Build intelligent search and task execution agents using ReAct, planning, and multi-agent frameworks (e.g., LangGraph, Dify, CrewAI)2. 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 citation3. LLM Training & Alignment: Conduct advanced post-training (SFT, RLHF, continual pretraining) and align models for reliable JSON-schema function calling and external tool usage4. Automated Evaluation & Iteration: Build offline/online evaluation pipelines using synthetic QA, retrieval metrics, and hallucination detection to continuously improve system performance and stability
Qualifications1. Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related field2. 3+ years of experience developing AI systems, with a focus on RAG, Agent architectures, or LLM training/optimization3. Proficiency in Python and key ML frameworks (PyTorch/TensorFlow), with experience in distributed training and high-performance inference4. 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 techniques5. 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