About The Position

We are currently seeking a highly motivated Infrastructure Software Engineer. The ideal candidate should possess a strong background in Java Spring Boot backend development and React JS frontend development, combined with experience leveraging AI-assisted development tools and LLMs to enhance engineering workflows. They should be passionate about creating modern, intelligent, user-friendly applications. In addition to technical expertise, excellent communication and interpersonal skills are crucial. The ideal candidate should be able to collaborate effectively with business partners from diverse backgrounds and varying technical expertise levels. As an Infrastructure Software Engineer, you will collaborate with multi-functional teams to develop, build, and deploy Java Spring Boot-based microservices and React-based web applications, increasingly augmented by AI-assisted development tools and intelligent agents.

Requirements

  • Bachelor’s degree in Computer Science, a technical field or a minimum of 5 years of relevant work experience.
  • Proficiency in React and related frontend technologies, including experience with Redux and React Router.
  • Experience in Unix, Oracle, and NoSQL databases like MongoDB.
  • Knowledge of containerization (Docker, Kubernetes) and CI/CD tools.
  • Hands-on experience in crafting and developing user interfaces, prioritizing responsiveness, accessibility, and a user-friendly experience.
  • Experience using AI-powered coding assistants and LLM-based tools like Claude to enhance development productivity and code quality.

Nice To Haves

  • Over 5 years of experience in developing Java/J2EE-based web applications.
  • Experience working in Data Center Infrastructure Management domain is a plus.
  • Experience building or integrating AI agents and intelligent automation into applications or developer workflows.
  • Familiarity with Claude skills, prompt engineering, MCP (Model Context Protocol), or similar agent orchestration patterns.
  • Understanding of responsible AI practices, including output validation and human-in-the-loop design patterns.
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