Job Description
Backend Development Engineer (AI Agent Direction) / AI Native Backend Engineer
About This Role:
We are seeking a backend engineer who embraces the "Agent Native" philosophy. Unlike traditional CRUD development, you will be responsible for designing and building intelligent agent systems capable of autonomous planning, memory retention, tool invocation, and environmental interaction.
We don’t expect you to treat AI as an occasionally called API; instead, we require you to reconstruct backend logic with state machines, graph computation (Graph), and autonomous decision-making as core paradigms. Here, every line of code you write may determine how an Agent understands complex tasks and breaks them down for execution.
Key Responsibilities
- Core Framework Development: Design and implement a highly scalable AI Agent execution engine supporting various reasoning modes such as ReAct, Plan-and-Execute, and multi-agent collaboration.
- Tool & Ecosystem Integration: Develop "hands and feet" for Agents—seamlessly encapsulate internal APIs, third-party services, and databases into standardized tools via Function Calling/Tool Use mechanisms.
- Memory System Construction: Design a hybrid memory architecture, including long-term memory (vector database-based) and short-term working memory (Redis-based).
- Workflow Orchestration: Use LangGraph, DSPy, or custom DSL (Domain-Specific Language) to orchestrate complex Agent workflows, handling loops, retries, backtracking, and human-in-the-loop collaboration.
- Performance Optimization: Optimize LLM call latency and costs (caching, prompt compression, model routing) while ensuring the stability of asynchronous task queues under high concurrency.
Job Requirements
- Education & Experience: Computer-related degree with 2-5 years of backend development experience.
- Solid Backend Foundation:
- Proficiency in at least one of Python/Go/Java (Python preferred due to mature AI ecosystem).
- Expertise in asynchronous programming (e.g., Python asyncio, Go Goroutine).
- Familiarity with frameworks like FastAPI/Spring Boot and ability to independently design RESTful/gRPC APIs.
- Databases & Middleware:
- Proficiency in PostgreSQL/MySQL and understanding of at least one vector database (Milvus/Pinecone/Qdrant/Chroma) for basic use cases (no tuning required, but knowledge of vector retrieval is essential).
- AI Fundamentals:
- Deep understanding of LLM limitations (hallucinations, context windows, reasoning bottlenecks) and ability to mitigate them through engineering.
- Familiarity with Prompt Engineering (Few-shot, Chain-of-Thought) in practical applications.
Benefits
- Team-building activities
- Health check-ups
- Annual bonuses
