2mo ago

Lead Engineer, AI Agent Systems

China·ShanghaiFull Timelead

OtherEngineer

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Quick Summary

Overview

Architecture Leadership and Evolution Lead the architecture and evolution of next-generation agent infrastructure designed for complex, knowledge-intensive work.

Technical Tools

OtherEngineer

Architecture Leadership and Evolution

Lead the architecture and evolution of next-generation agent infrastructure designed for complex, knowledge-intensive work.

Define clear boundaries and collaboration mechanisms across three core layers: the execution engine, context and reasoning orchestration, and the agent capability foundation. Ensure high availability, reliability, and long-term extensibility in environments with a low tolerance for hallucinations and incorrect outputs.

Design and implement the Agent Loop runtime and its middleware pipelines.

Lead the execution and orchestration of planning and sub-agent workflows, including task decomposition, dependency management, concurrency control, and execution scheduling.

Build mechanisms for checkpointing, interruption and resumption, failure recovery, self-healing, authorization, and cost control to ensure the reliable execution of long-running and complex multi-step tasks.

Own the design and implementation of core context orchestration capabilities.

Develop strategies for input standardization, dynamic capability representation, and hierarchical context-budget management, including structured degradation when resource or context limits are reached.

Build structured task workspaces that support efficient organization of dynamic context. Address challenges including long-history compression, tool-output normalization, evidence traceability, and the management of information across different stages of a task.

Lead the development of foundational agent capabilities, including:

Secure sandboxed environments using technologies such as Docker, Kubernetes, and AST-based controls

Multi-layer memory stores

Retrieval and knowledge-access capabilities

An MCP (Model Context Protocol) Hub

Skill execution and management engines

File-processing and transfer pipelines

Multi-tenant isolation and security controls

End-to-end observability and diagnostics

Remain hands-on and personally contribute code to critical platform modules.

Lead technical decomposition, architecture decisions, code reviews, and the development of automated evaluation systems and feedback loops.

Guide the engineering team in translating specific business use cases into reusable platform and infrastructure capabilities.

Engineering and Leadership Experience

At least five years of professional software engineering experience.

Proven experience leading the design and delivery of complex software systems beyond standard CRUD applications or basic integrations with AI APIs.

Demonstrated experience operating as a Tech Lead, Staff Engineer, or equivalent technical leader.

Experience leading an engineering team of at least three people.

Strong Python software-engineering skills and the ability to independently own critical platform modules.

Deep experience with common engineering challenges such as streaming responses, asynchronous and concurrent execution, and multi-model routing and provider integration.

Strong judgement in balancing system reliability, security, cost, latency, and delivery speed.

Solid understanding of distributed systems, production architecture, debugging, and operational reliability.

Candidates must have substantial hands-on engineering experience with Agent and LLM systems, with deep expertise in at least two of the following three areas:

Execution Engine

Multi-step reasoning loops

Tool lifecycle management

Planning and sub-agent orchestration

Interruption and resumption

Failure recovery and self-healing

Context and Reasoning Orchestration

Input standardization

Context assembly

Context and token-budget governance

Provider-specific request shaping

Task-stage modelling