AI Engineering Productivity Expert

About the job

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