Full-Stack Engineer(Server)

工作說明

•Design and implement a cloud-based data center management platform
•Participate in architecture decisions — evaluate trade-offs between complexity, cost, and maintainability; push back on premature abstraction
•Build REST APIs that are clean, consistent, and versioned from the start
•Collaborate with the UI/UX team to translate designs into frontend implementation, leveraging AI tools to accelerate code generation and iteration
•Review and integrate AI-generated code, ensuring it meets quality, performance, and consistency standards
•Leverage AI tools throughout the development lifecycle to improve velocity without sacrificing code quality
•Collaborate with engineers and product stakeholders to translate requirements into pragmatic, well-scoped solutions
•Maintain awareness of when to defer infrastructure complexity versus when the problem actually demands it

需求條件

Job Requirements
•Bachelor's degree in Computer Science, Software Engineering, or equivalent practical experience
•3+ years of experience building full-stack systems with Python (FastAPI) backend
•Solid grasp of software architecture principles — service decomposition, API boundary design, data modeling, and scalability trade-offs
•Experience designing and consuming RESTful APIs with attention to versioning, auth, and error handling
•Familiarity with system design and cloud deployment patterns (containerization, managed services, infrastructure trade-offs)
•Proficient with AI-assisted development tools (e.g. Cursor, Claude Code, Codex) — able to apply them effectively to accelerate implementation, code review, and design iteration

Nice-to-Have Skills
•Experience with cloud platforms (AWS, Azure, GCP) and understanding of when to use managed vs. self-hosted services
•Familiarity with data center management protocols (e.g. Redfish/DMTF)
•Knowledge of time-series or analytics databases (ClickHouse, Prometheus)
•Familiarity with modern frontend frameworks sufficient to collaborate with UI/UX designers and review AI-generated code
•Experience with data analysis pipelines or building analytics features on top of structured operational data
•Familiarity with LLM APIs (e.g. OpenAI, Anthropic) and integrating AI features into backend services
•Understanding of RAG patterns, embedding-based search, or anomaly detection applied to time-series or log data
•Awareness of data privacy considerations when processing customer operational data for AI/analytics workloads