Applied AI Agent/AI Productivity Engineer
New
B
BinanceAI software
Location: Australia; Hong Kong; Malaysia; New Zealand; Singapore; TaiwanFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 1–5 years of software engineering or applied AI engineering experience.
- Required Skills
- PythonJavaGoPrompt Engineering
Requirements
- 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.
- Proficiency 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, including working with engineering teams to understand and solve real-world problems.
Responsibilities
- Design and develop AI-powered productivity tools for internal engineering teams, including coding assistants, knowledge retrieval systems, developer tools and workflow automation.
- Build and integrate LLM-powered applications for engineering use cases such as code generation, code understanding, testing and developer workflow automation.
- Develop AI Agent workflows and supporting infrastructure, including tool calling, context management, memory, evaluation and workflow orchestration.
- Integrate AI capabilities into the software development lifecycle, from requirements and development to testing and deployment.
- Work with engineering teams to identify development bottlenecks and translate them into practical AI-driven solutions.
- Apply Prompt Engineering, RAG, AI Agents and MCP/tool calling, and evaluate emerging AI technologies for practical adoption.
- Own projects end-to-end, from problem definition and technical design to implementation, deployment and iteration.
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