Site Reliability Engineer: AI-Driven RAG Systems

Binance; Hong Kong; On-site; HKD 480,000 - 900,000; Full time

Benefits offered by this job

Work-from-home arrangement

Competitive salary and benefits

Learning & development opportunities

Job summary

Binance is seeking an experienced AI/ML engineer to design and operate Agentic RAG pipelines, implement memory-enhanced retrieval, and lead multi-agent task execution in production.

You will collaborate with researchers and engineers to define model-capability innovations, build benchmarks, and apply end-to-end retrieval improvements across domains within a global, fast-paced environment.

Qualifications

• 2-8+ year hands-on experience with LLM, RAG and AI agent systems in production.
• Hands-on experience building production retrieval pipelines end-to-end; embedding models and vector stores.
• Experience with Agent Harness runtimes and multi-tenant runtimes; plan/execute loops.

Responsibilities

• Design and operate retrieval pipelines with adaptive, multi-hop retrieval workflows.
• Collaborate with researchers to implement model-capability-driven innovations including context management and long-term memory.
• Propose benchmarks; construct datasets and measure agent intelligence across domains; improve retrieval performance.

Skills

LLM & RAG

Agent Harness

Memory & Tools

Prompt & Context

Multi-agent systems

Independent research

AI-native engineering

Heavy agent user

Tools

Qdrant

Milvus

Pinecone

Weaviate

Pi Agent

AgentScope 2.0

Job description

Binance is seeking an experienced AI/ML engineer to design and operate Agentic RAG pipelines, implement memory-enhanced retrieval, and lead multi-agent task execution in production.

You will collaborate with researchers and engineers to define model-capability innovations, build benchmarks, and apply end-to-end retrieval improvements across domains within a global, fast-paced environment.