AI Agent Architect

About Us

Established in 2018, Bybit is one of the world’s leading cryptocurrency exchanges and digital financial platforms, serving over 80 million users across more than 200 countries and regions. Powered by world-class technology and a user-first mindset, Bybit delivers a seamless ecosystem across trading, payments, wealth management, custody, institutional services, and Web3 - connecting users to the future of digital finance.

Our core values define how we build. We listen, care and improve to create products and experiences that put users first. Backed by a global team of ambitious builders, problem-solvers, and innovators, we foster a high-performance and fast-moving environment where talent is empowered to drive real impact at the global scale. Supported by 24/7 multilingual customer service and a strong commitment to innovation, we are shaping the future of finance through technology, collaboration, and bold execution.

Today, Bybit is recognized as one of the most trusted and transparent platforms in the digital asset industry, continuing to expand its global presence while building the infrastructure for the next generation of financial services.

Job Overview

We are at a historical turning point where AI is reshaping financial infrastructure. As an AI Agent Architect, you will be a core builder of our AI intelligent system - not just writing code, but defining how next-generation financial-grade agents perceive market signals, plan execution paths, maintain business context memory, and collaborate across systems. You will lead the creation of an agent ecosystem capable of handling real-world financial scenarios, covering core businesses such as trading assistance, risk assessment, compliance monitoring, and intelligent user services, enabling AI to autonomously complete complex tasks in the high-speed, high-pressure, and highly compliant environment of crypto finance. Here, there are no vaguely defined roles, but rather technical decision-making power that directly impacts the experience of hundreds of millions of users, alongside an elite engineering team with a high-density understanding of LLM and Agent technologies.

Core Responsibilities

1.

Agent System Architecture Design: Lead the design of the AI agent architecture, define the operating model, lifecycle management, and resource isolation strategies for agents on the Openclaw platform; build the agent capability foundation to support core financial scenarios such as trading assistance, risk assessment, and compliance monitoring, ensuring the system runs stably under conditions of high concurrency, low latency, and strict compliance constraints.

2.

Financial Scenario Agent Engineering implementation: Convert business requirements such as trading signal analysis, risk identification, regulatory compliance, and intelligent customer service into executable Agent workflows; design task planning pipelines (ReAct / Plan-and-Execute), exception fallback mechanisms, and result verification systems to drive Agents quickly from PoC to production environments.

3.

Openclaw platform construction and evolution: Deeply participate in the architectural evolution and performance optimization of the Openclaw Agent Runtime; design plugin systems, capability registration centers, and multi-Agent orchestration mechanisms to reduce onboarding costs for business teams and continuously improve the platform's scalability and reuse rate.

4.

Tool ecosystem and internal system integration: Build a standardized tool calling framework to connect Bybit's internal trading engine, risk control systems, data platforms, and external APIs; design tool permission controls, call chain tracking, and error recovery mechanisms; integrate standard protocols like MCP to build a financial-grade heterogeneous tool ecosystem.

5.

Production quality and observability system: Lead the construction of observability for the Agent system, including Trace tracking, Evaluation, cost monitoring, and latency management; establish Agent quality baselines and regression testing systems for financial scenarios to continuously improve system stability and explainability.

6.

Technical foresight and team empowerment: Continuously track frontier developments in the Agent field and translate research findings into AI engineering practices; establish team Agent development standards and best practices, and promote the overall improvement of the team's AI-native engineering capabilities through Code Reviews, technical sharing, and roadmap reviews.

Qualifications

1.

Solid LLM/Agent engineering experience: Possess 3+ years of experience in AI system development, including at least 1 year of experience in production-level implementation of Large Language Models (LLM), RAG, or Agent systems; have practical experience in developing and optimizing the Openclaw platform, be familiar with its Agent Runtime architecture, plugin systems, and multi-Agent orchestration mechanisms, and be able to independently complete the engineering implementation and performance tuning of complex Agent applications based on Openclaw.

2.

System architecture capabilities: Possess distributed system design capabilities, and be familiar with engineering patterns such as microservice architectures, message queues (Kafka / RabbitMQ), and asynchronous task scheduling; capable of independently completing the full decision-making link from requirements analysis and architectural design to technology selection.

3.

Programming skills: Proficient in at least one backend language (Golang / Java / Node.js); possess capabilities in REST API design and Docker / Kubernetes containerized deployment.

4.

Understanding of core LLM technology: Deep understanding of Transformer architecture, Attention mechanisms, and Prompt Engineering (CoT, Few-shot, Structured Output); familiar with mainstream model APIs (OpenAI, Anthropic Claude, Google Gemini) and local deployment solutions (Ollama, vLLM).

5.

Engineering Quality Awareness: Possess good code design habits (design patterns, unit testing, CI/CD); have practical experience in building LLM Evaluation systems or Agent Tracing (LangSmith / Phoenix / Langfuse).

Bonus Items

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Have research or engineering experience in planning/reasoning combining MCTS, A*, or Reinforcement Learning (RL) with LLMs; familiar with SFT / RLHF training workflows.

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Have experience in deploying Agent systems in Finance, Web3, Compliance, or other high-complexity vertical domains; understand the special constraints domain knowledge imposes on Agent design.

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Have open-source contributions or have published papers related to Agent / RAG / LLM at top conferences such as NeurIPS / ICML / ACL / ICLR.

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Possess experience in Multimodal Agent development, including unstructured data processing capabilities such as image-text understanding and document parsing (PDF / financial reports / research reports).

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Familiar with MCP (Model Context Protocol) or have participated in the design and implementation of Agent standard protocols.

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 Bybit's internal trading engine, 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

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Experience combining MCTS, A\*, or Reinforcement Learning (RL) with LLMs for planning and reasoning; familiarity with SFT / RLHF training pipelines.

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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.

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Experience with multimodal Agent development, including vision-language understanding and unstructured document parsing (PDFs, financial reports, research papers).

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Familiarity with MCP (Model Context Protocol) or prior involvement in designing and implementing Agent communication standards.

Why Join Us At Bybit, 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.