AI Algorithm Engineer; Agent Specialization
Job Description & How to Apply Below
Job Introduction
We 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 Responsibilities- Develop AI Agent Systems:
Build intelligent search and task execution agents using ReAct, planning, and multi-agent frameworks (e.g., Lang Graph, Dify, CrewAI) - Optimize End-to-End RAG Pipelines:
Build and refine efficient RAG systems from ingestion, chunking, and embedding to hybrid vector search (Open Search), 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
- 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/Tensor Flow), 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 Open Search/vector databases
- AI Agent framework development (Lang Graph, 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
- 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
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