AI Engineering Productivity Expert

Job description

**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. ## Core Responsibilities ### AI Engineering Productivity Strategy - Define the technical roadmap and implementation strategy for AI for SDLC, aligning AI capabilities with engineering productivity goals. - Analyze the software development lifecycle and identify key bottlenecks affecting productivity, quality, and developer experience. - Design scalable AI-powered engineering solutions and drive their adoption across the organization. - Establish a measurable evaluation framework to assess improvements in development cycle time, delivery quality, and engineering productivity. - Track developments in AI Agents, intelligent coding, and AI-powered software engineering, evaluating their maturity and practical value. ### AI for SDLC Solutions - Drive the application of AI across requirements analysis, technical design, coding, code review, testing, release, operations, and incident analysis. - Design and build AI Coding Agents for enterprise software development scenarios. - Integrate Agents with code repositories, knowledge bases, engineering toolchains, CI/CD systems, and observability platforms. - Explore end-to-end AI-assisted workflows covering requirement understanding, code generation, testing, validation, and delivery. - Build capabilities for code understanding, change analysis, intelligent code review, automated testing, defect remediation, and incident diagnosis. - Continuously evaluate and improve the accuracy, reliability, and developer experience of AI engineering tools. ### AI Agent & Multi-Agent Architecture - Design AI Agent architectures and develop core capabilities, including planning, task decomposition, tool use, context management, memory, reflection, and result validation. - Design and implement multi-agent architectures covering role assignment, task orchestration, shared state, conflict resolution, failure recovery, and result aggregation. - Conduct in-depth research into the use of Agents for large codebases, complex software engineering tasks, and long-running workflows. - Address reliability, controllability, observability, and maintainability challenges in production Agent systems. - Establish an Agent evaluation framework covering task completion rate, code correctness, test pass rate, execution efficiency, security, and human intervention rate. - Promote the adoption of MCP, A2A, or similar tool, context, and Agent collaboration protocols within the engineering ecosystem. ### Platform Engineering & Scaled Adoption - Contribute to an enterprise AI engineering productivity platform that provides standardized and reusable Agent capabilities. - Design a unified architecture connecting tools, prompts, workflows, knowledge bases, context, and access-control mechanisms. - Support the integration, evaluation, and continuous improvement of AI Coding Agents and intelligent engineering tools. - Integrate AI capabilities with IDEs, source-code management platforms, project management systems, CI/CD pipelines, and internal developer platforms. - Drive organization-wide adoption through pilot projects, measurable validation, and documented best practices. - Partner with development, QA, platform, security, and operations teams to improve engineering processes and culture. ### Security & Governance - Establish quality, security, and compliance mechanisms for AI-generated code. - Design access controls, sensitive-data protection, operational auditing, and approval mechanisms for high-risk actions. - Mitigate risks related to source-code and data leakage, prompt injection, software supply chains, and unauthorized Agent actions. - Define clear boundaries between autonomous Agent execution and human approval to ensure safe, controlled, and traceable operations. - Develop usage guidelines, review standards, and production-readiness requirements for AI Coding Agents. - Requirements - 8+ years of experience in software development, engineering productivity, developer platforms, or related engineering roles. - Deep understanding of the full software development lifecycle, including requirements analysis, architecture, development, testing, code review, CI/CD, release, and operations. - Strong software engineering and system design capabilities, with the ability to solve engineering problems in large codebases and complex systems. - Proficiency in at least one mainstream programming language such as Python, Go, Java, or TypeScript. - Deep understanding of large language models, RAG, prompt engineering, function calling, and AI Agent fundamentals. - Hands-on experience developing AI Agents, including planning, tool use, context engineering, memory, and evaluation. - In-depth experience using one or more AI Coding Agents, with a clear understanding of their capabilities and limitations in real-world development workflows. - Strong understanding of and practical experience with multi-agent architectures, including orchestration, collaboration, shared state, conflict handling, and failure recovery. - Familiarity with Git, source-code management platforms, CI/CD, containers, and modern engineering toolchains. - Ability to translate AI capabilities into measurable improvements in engineering productivity, software quality, and business value. ### Nice to Have - Experience leading the development of an AI for SDLC platform, intelligent engineering platform, or enterprise AI Coding Agent. - Experience with large-codebase understanding, code generation, automated remediation, intelligent code review, or automated testing. - Familiarity with software engineering Agent benchmarks such as SWE-bench, or experience building internal Agent evaluation systems. - Familiarity with MCP, A2A, LangGraph, AutoGen, CrewAI, or similar Agent frameworks and protocols. - Experience building internal developer platforms, engineering productivity platforms, or developer experience initiatives. - Familiarity with engineering productivity frameworks such as DORA and SPACE. - Open-source contributions, patents, publications, or demonstrated influence in relevant technical communities. --- ## Soft Skills - Ability to proactively define problems and drive execution in ambiguous environments. - Strong interest in emerging technologies while maintaining a practical focus on reliability, maintainability, and business outcomes. - Sound technical judgment and the ability to balance effectiveness, complexity, security, and delivery speed. - Strong cross-functional communication skills and the ability to collaborate with engineering, QA, security, platform, operations, and leadership teams. - A data-driven mindset that evaluates AI through measurable outcomes rather than demos or subjective impressions. - Strong ownership and technical influence, with the ability to drive changes in engineering processes and culture. --- ## This Role Is Not for You If… - You are more interested in AI demonstrations than solving real software engineering problems. - You have only used AI coding tools but have no experience developing, integrating, or evaluating Agents. - You believe AI-generated code does not require testing, code review, or security governance. - You pursue full automation without considering risk controls, result validation, or human collaboration. - You prefer waiting for clearly defined requirements instead of proactively identifying engineering productivity problems. --- ## Why Join Us - Build an enterprise AI engineering productivity capability from the ground up. - Explore real-world applications of AI Coding Agents and multi-agent systems in complex software engineering environments. - Collaborate with strong engineering, platform, security, and AI engineering teams. - Transform software development in measurable ways and create organization-wide impact. **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.