AI工程师

omniXM builds intelligent customer experience management products. Our flagship AI product omniXMOS includes a conversational survey analysis engine omniSense that processes real-world business feedback at scale. The system combines deterministic data tools, LLM-powered reasoning, multi-modal report generation, and document-grounded RAG — all served through a FastAPI backend and deployed on Azure. We are growing our AI Engineering team to deepen the intelligence layer of omniSense: smarter agents, richer retrieval, better-adapted models, and tighter orchestration.

2+ years of hands-on experience building and shipping AI/LLM-powered systems in production. Proficiency in Python and async web frameworks (FastAPI or equivalent). Practical experience with multi-agent frameworks: LangGraph, LangChain, CrewAI, Google ADK or equivalent; understanding of ReAct reasoning loops, tool calling, and agent state management. Hands-on RAG implementation experience: embedding models (OpenAI, sentence-transformers), vector stores (FAISS, ChromaDB, Qdrant, Milvus), chunking, retrieval tuning. Familiarity with LLM fine-tuning: LoRA / QLoRA via Hugging Face PEFT, dataset preparation, training on GPU infrastructure. Experience working with OpenAI / Gemini / Anthropic APIs and open-source models (LLaMA, Mistral). Solid understanding of prompt engineering: chain-of-thought, few-shot, structured output, function/tool calling schemas. Experience with streaming APIs (SSE / WebSocket) for real-time LLM output delivery. Familiarity with Azure or other cloud platforms for deployment and storage (Blob, App Service, AKS).

omniXM builds intelligent customer experience management products. Our flagship AI product omniXMOS includes a conversational survey analysis engine omniSense that processes real-world business feedback at scale. The system combines deterministic data tools, LLM-powered reasoning, multi-modal report generation, and document-grounded RAG — all served through a FastAPI backend and deployed on Azure.