[Hiring] Applied AI Agent/AI Productivity Engineer @Binance

Role Description

Design and develop AI-powered productivity tools for internal engineering teams, including:

• Coding assistants
• Knowledge retrieval systems
• Developer tools
• Workflow automation

Build and integrate LLM-powered applications for real-world engineering use cases, such as:

• Code generation
• Code understanding
• Testing
• Developer workflow automation

• Develop AI Agent workflows and supporting infrastructure, including:
• Tool calling
• Context management
• Memory
• Evaluation
• Workflow orchestration
• Integrate AI capabilities into the software development lifecycle, from requirements and development to testing and deployment, to improve engineering productivity.
• Work closely with engineering teams to identify development bottlenecks and translate them into practical AI-driven solutions.
• Apply approaches such as Prompt Engineering, RAG, AI Agents, and MCP/tool calling, and continuously evaluate emerging AI technologies for practical adoption.
• Own projects end-to-end, from problem definition and technical design to implementation, deployment, and iteration.

Qualifications

• Bachelor's degree or above in Computer Science, Data Science, Software Engineering or a related field.
• 1–5 years of software engineering or applied AI engineering experience.
• Proficient in at least one programming language, such as Python, Java, Go, or JavaScript/TypeScript.
• Solid software engineering fundamentals, including API integration, system design, debugging, and testing.
• Hands-on experience building or integrating LLM-powered applications.
• Practical understanding of one or more of LLM Agents, RAG, Prompt Engineering, MCP/tool calling, context management, or AI evaluation.
• Experience with backend, platform, or full-stack development and integrating AI capabilities into production or internal workflows.
• Strong problem-solving and learning ability, with a hands-on and proactive approach.
• Good communication and collaboration skills, with the ability to work closely with engineering teams to understand and solve real-world problems.

Requirements

• Experience building AI productivity tools, developer tools, internal platforms, or workflow automation systems.
• Experience with AI Agent frameworks, Agent memory, tool orchestration, or multi-agent systems.
• Experience integrating LLMs into software development workflows, such as coding, testing, code review, or deployment.
• Experience with RAG, knowledge systems, search, or documentation platforms.
• Exposure to DevOps, CI/CD, cloud infrastructure, or engineering automation.
• Experience with LangChain, Claude Agent SDK, MCP, or similar AI tooling.
• Open-source contributions or an active GitHub portfolio.

Benefits

• Shape the future of finance.
• Collaborate with world-class talent in a user-centric global organization with a flat structure.
• Tackle challenging projects in a fast-paced and innovative environment with autonomy.
• Thrive in a results-driven workplace with opportunities for career growth and continuous learning.
• Competitive salary and company benefits.
• Remote-first, work-from-home arrangement (may vary depending on the nature of the role and team).