Job Responsibilities:

• Develop AI-powered tools for engineering teams (e.g., Incident Bot, DevOps Bot, OnCall Bot, Security Bot, Meeting Assistant, Automation Toolchains) to streamline workflows and address high-friction pain points in the R&D process, thereby enhancing engineering efficiency.
• Explore cost-effective deployment solutions for large language models (e.g., model compression, inference optimization) to achieve a balance between performance and engineering costs.
• Own the end-to-end development cycle, which includes requirements analysis, model fine-tuning (if applicable), implementation, and deployment optimization. - Collaborate with technical teams to transform tool outputs into reusable knowledge base assets.

Qualifications:

• Currently pursuing or holding a Bachelor’s degree or higher in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• Proficient in Python with a strong foundation in algorithms and data structures.
• Solid understanding of machine learning fundamentals, along with familiarity with LLM-related technologies such as Transformer architecture, prompt engineering, Retrieval-Augmented Generation (RAG), and Multi-component Prompting (MCP).
• Capable of rapid prototyping and experienced with at least one mainstream deep learning framework (e.g., PyTorch or TensorFlow).
• Strong learning ability, curiosity to explore unfamiliar areas, and excellent communication and logical thinking skills.
• In-depth understanding of the LLM technology stack (e.g., fine-tuning evaluation, inference optimization, agent framework design) with hands-on experience in at least one complete project is preferred.
• Familiarity with frontend-backend integration (e.g., FastAPI or Streamlit) and the ability to independently deploy basic demos is preferred.
• Bilingual in Mandarin Chinese is required.

Base salary for his position is $18/hr

• Develop AI-powered tools for engineering teams (e.g., Incident Bot, DevOps Bot, OnCall Bot, Security Bot, Meeting Assistant, Automation Toolchains) to streamline workflows and address high-friction pain points in the R&D process, thereby enhancing engineering efficiency.
• Explore cost-effective deployment solutions for large language models (e.g., model compression, inference optimization) to achieve a balance between performance and engineering costs.
• Own the end-to-end development cycle, which includes requirements analysis, model fine-tuning (if applicable), implementation, and deployment optimization. - Collaborate with technical teams to transform tool outputs into reusable knowledge base assets