Responsibility

• Contribute to backend and/or full-stack feature development, testing, and release for core products/platforms, delivering production-ready code.
• Collaborate with senior engineers on module design, API/interface definition, system integration, and deployment (including logging, monitoring, and performance optimization).
• Write and maintain automated tests and CI/CD-related configurations to improve development efficiency and code quality.
• Support the engineering deployment of AI/LLM capabilities based on business needs, such as integrating LLM APIs, building basic RAG pipelines, and embedding tool/agent capabilities into existing systems.
• Help build and maintain datasets and benchmarks for evaluation/regression, track online performance, and assist with debugging and fixing issues.
• Produce necessary technical documentation and communicate effectively across teams.

Requirements

• Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field.
• Solid software engineering fundamentals: data structures and algorithms, object-oriented design, common design patterns, and coding best practices.
• Proficient in at least one primary programming language for engineering work; Python is required. Experience with Java/Go/TypeScript/JavaScript is a plus.
• Familiar with common engineering tools and workflows: Git, basic Linux, debugging skills; understanding of unit/integration testing, RESTful APIs, and databases (SQL/NoSQL).
• Some AI/LLM project or internship experience is sufficient (no deep research required), e.g., prompt engineering/function calling, RAG, vector databases, or experience integrating frameworks like LangChain/LangGraph is a plus.
• Strong ownership, good communication and collaboration skills, and the ability to learn quickly and deliver.
• Able to commit to at least 6 months, 5 days per week.

Nice to Have

• Experience shipping web/backend services; knowledge of caching, message queues, concurrency, and performance tuning.
• Familiarity with containerization and deployment (Docker/Kubernetes), cloud platforms (AWS/GCP/Azure), or observability (Prometheus/Grafana/ELK).
• Exposure to LLM evaluation, prompt iteration, data feedback loops, or basic fine-tuning workflows.