Lead Engineer, AI Agent Systems
Patsnap is transforming professional knowledge work with AI. Built on our deep expertise in global patent, R&D, and innovation data, we are developing AI Agent products for complex workflows such as patent search, analysis, drafting, and prosecution.
We are looking for a Lead Engineer, AI Agent Systems, who will provide hands-on technical leadership in architecting and building next-generation agent infrastructure for complex, knowledge-intensive work. This role will lead the evolution of core agent systems spanning execution and orchestration, context and reasoning, and foundational capabilities such as memory, retrieval, secure sandboxing, skills, and observability. Working closely with the engineering team, this leader will set key architectural direction, contribute directly to critical platform modules, and turn real-world business requirements into reliable, scalable, and reusable agent capabilities.
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This is a in office position in our Shanghai, China office.
Who are we?
Patsnap is a global, pre-IPO company that transforms the way organizations harness their Intellectual Property and Research & Development productivity. Our platform revolutionizes how IP and R&D teams collaborate across the entire innovation lifecycle, using domain-specific AI to accelerate the creation of market-ready products. With over 12,000 customers worldwide, including some of the biggest names in innovation, Patsnap is at the forefront of technological advancement. Our $300M Series E funding round brings our valuation to a $1 billion unicorn status, and we still have a remarkable amount of growth ahead.
We have a vibrant and diverse team with offices in Singapore, Toronto, London, Shanghai and remote teams based in US. Our hyper-growth trajectory is powered by our people, and we are extremely proud of our company-wide vision, work ethic, and entrepreneurial spirit. We are committed to fostering an inclusive environment where talent thrives and ideas bloom.
What You'll Be Doing:
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.
Agent Execution Engine
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.
Context and Reasoning Orchestration
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.
Agent Capability Foundation
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
Technical Leadership and Team Enablement
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.
Requirements
What We'd Love From You:
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.
Core Engineering Capabilities
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.
Depth in AI and Agent Systems
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
Long-context compression and evidence traceability
Agent Capability Foundation
Sandbox isolation
Memory and retrieval systems
MCP infrastructure
File-system and file-processing capabilities
Multi-tenant isolation
Security, monitoring, and observability