AI Engineering Productivity Expert

About Us

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.

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  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.

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  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.

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  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.