AI Algorithm Engineer Agent Specialization

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., 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

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