AI Algorithm Engineer (Agent Specialization)

Full Description

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