AI Algorithm Engineer (Agent Specialization)

AI Algorithm Engineer (Agent Specialization)

Location: Global / Hong Kong / Singapore / Dubai
Department: CMC - Tech
Commitment: Full-time
Workplace Type: Remote

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 solutions.

Core Responsibilities

1. 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 citation.
3. LLM Training & Alignment: Conduct advanced post-training (SFT, RLHF, continual pretraining) and align models for reliable JSON-schema function calling and external tool usage.
4. 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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