Design and develop AI productivity tools for internal engineering teams, such as coding assistants, knowledge retrieval systems, and workflow automation tools
Build LLM-powered applications for development scenarios, including code generation, code understanding, and test generation capabilities
Integrate AI capabilities across the full software development lifecycle — from requirements and development to testing and deployment — to improve overall engineering efficiency
Collaborate closely with engineering teams to identify bottlenecks in development workflows and provide AI-driven optimization solutions
Explore and implement technical approaches including Prompt Engineering, RAG, and AI Agents Continuously track and adopt emerging AI technologies to optimize internal platform capabilities
Requirements
Bachelor's degree or above in Computer Science or a related field
Proficient in at least one programming language, such as Python, Java, Go, or JavaScript
Solid foundation in data structures and algorithms
Basic understanding of large language models (LLMs) and their practical applications
Experience using AI tools such as ChatGPT, Copilot, or similar
Familiarity with backend development or web development fundamentals
Understanding of API integration and system integration principles
Strong problem-solving ability, with a proactive, result-oriented work style
Good communication and collaboration skills
Bonus Points
Hands-on project experience with Prompt Engineering, RAG, or AI Agents
Experience building AI tools, developer tools, or internal platforms
Exposure to DevOps, CI/CD, or engineering automation
Experience with knowledge systems, search systems, or documentation platforms
Open-source contributions or an active GitHub portfolio