AI Agent Architect

About the Role
We're at an inflection point where AI is fundamentally reshaping financial infrastructure. As an AI Agent Architect, you will be a core builder of AI's intelligent ecosystem — not just writing code, but defining how next-generation financial-grade Agents perceive market signals, plan execution paths, retain business context, and orchestrate across systems. You will lead the construction of Agent capabilities purpose-built for real financial scenarios: trading intelligence, risk assessment, compliance monitoring, and smart user services — engineered to operate autonomously in crypto finance's high-velocity, high-stakes, and high-compliance environment. This is a role with genuine technical ownership. You'll make architectural decisions that directly shape the experience of hundreds of millions of users, alongside an elite engineering team with exceptional depth in LLM and Agent technology.

Key Responsibilities

1. Agent Architecture Design: Lead the architectural design of AI's intelligent Agent layer — defining agent execution models, lifecycle management, and resource isolation strategies on the Openclaw platform. Build the foundational Agent capabilities powering core financial scenarios including trading intelligence, risk assessment, and compliance monitoring, engineered for high-concurrency, low-latency, and regulatory-grade reliability.
2. Financial Scenario Agent Engineering: Translate complex financial use cases — trading signal analysis, risk identification, regulatory compliance, and intelligent customer service — into production-ready Agent workflows. Design task planning chains (ReAct / Plan-and-Execute), fallback and error recovery mechanisms, and result validation frameworks that accelerate the path from PoC to production.
3. Openclaw Platform Development & Evolution: Contribute deeply to the architectural evolution and performance optimization of the Openclaw Agent Runtime. Design the plugin ecosystem, capability registry, and multi-agent orchestration mechanisms to reduce onboarding friction for product teams and improve platform extensibility and reusability.
4. Tool Ecosystem & Internal System Integration: Build a standardized tool-calling framework connecting internal trading engines, risk systems, data platforms, and external APIs. Design tool permission controls, call-chain tracing, and error recovery; integrate standards such as MCP to unify a financial-grade heterogeneous tool ecosystem.
5. Production Quality & Observability: Lead the observability stack for Agent systems, including trace monitoring, evaluation pipelines, cost tracking, and latency governance. Establish quality baselines and regression testing frameworks for financial Agent scenarios, continuously improving system stability and interpretability.
6. Technical Vision & Team Enablement: Track and internalize cutting-edge Agent research, translating advances into AI engineering practice. Define team-wide Agent development standards and best practices; drive AI-native engineering capability uplift through code reviews, technical sharing, and architectural reviews.

Requirements

1. Proven LLM / Agent Engineering Experience: 3+ years of AI systems development experience, including 1+ year of production experience with Large Language Models (LLMs), RAG systems, or Agent frameworks; hands-on development and optimization experience with Openclaw, including familiarity with its Agent Runtime architecture, plugin ecosystem, and multi-agent orchestration mechanisms.
2. Systems Architecture Proficiency: Strong distributed systems design skills including microservices architecture, message queues (Kafka / RabbitMQ), and async task scheduling; capable of independently driving the full decision chain from requirements analysis through architecture design to technology selection.
3. Programming Expertise: Proficiency in at least one backend language (Golang / Java / Node.js); solid REST API design and containerized deployment skills (Docker / Kubernetes).
4. Deep LLM Technical Understanding: Strong grasp of Transformer architecture, Attention mechanisms, and advanced Prompt Engineering (CoT, Few-shot, Structured Output); experience with major model APIs (OpenAI, Anthropic Claude, Google Gemini) and local deployment solutions (Ollama, vLLM).
5. Engineering Quality Standards: Strong software design fundamentals (design patterns, unit testing, CI/CD); hands-on experience building LLM evaluation frameworks or Agent tracing pipelines (LangSmith / Phoenix / Langfuse).

Nice to Have

• Experience combining MCTS, A*, or Reinforcement Learning (RL) with LLMs for planning and reasoning; familiarity with SFT / RLHF training pipelines.
• Proven Agent system deployments in high-complexity vertical domains such as Finance, Web3, or Compliance, with an understanding of domain-specific constraints on Agent design.
• Open-source contributions or publications in NeurIPS / ICML / ACL / ICLR on Agent, RAG, or LLM-related topics.
• Experience with multimodal Agent development, including vision-language understanding and unstructured document parsing (PDFs, financial reports, research papers).
• Familiarity with MCP (Model Context Protocol) or prior involvement in designing and implementing Agent communication standards.

Why Join Us
At the company, we are committed to fostering a supportive and enriching work environment.
Our benefits include:

• Study Growth Fund: We support your professional development and continuous learning.
• Internal Events: Participate in regular team-building activities, workshops, and events designed to promote collaboration and innovation.
• Global Collaboration: Be part of a diverse, international team, working alongside colleagues from around the world.
• Career Advancement: Access opportunities for growth and advancement within a rapidly expanding global company.
• Internal Mobility: Grow with us - Your long-term development is important to us. We offer internal job opportunities to help build your career path.