Backend Engineer (AI Prompt Engineering)

We're looking for an engineer who combines backend engineering ability with LLM/Prompt engineering ability — someone who both develops real-time in-class backend services and owns the design, debugging, and continuous optimization of teaching prompts and conversational ability, so the AI teacher speaks better, more stably, and more controllably. Responsibilities Design and develop real-time in-class teaching backend services, ensuring the conversation pipeline is stable, low-latency, and highly available.Own the design, debugging, and version management of teaching prompts (lesson types / question types / teacher scripts / multi-turn dialogue strategy), supporting real classroom teaching outcomes.Design and implement engineering capabilities for LLM invocation: multi-model routing, streaming output, context management, Function/Tool Calling, timeout/retry/fallback (degradation), cost and latency optimization.Build an evaluation and regression system for prompt and dialogue effectiveness (metric design, bad-case analysis, A/B testing, regression sets), driving measurable, continuous improvement of teaching outcomes.Handle LLM stability and safety issues: hallucination suppression, output constraints, content safety/compliance filtering.Collaborate with instructional design, product, and algorithm teams to accurately translate teaching intent into controllable, reusable prompts and service capabilities. Requirements Backend Engineering Ability Bachelor's degree or above, Computer Science-related major, 5+ years of backend development experience.Proficient in Java (Spring ecosystem) or equivalent backend ability; familiar with microservices, high concurrency, and interface design; has JVM tuning experience.Familiar with real-time/streaming pipeline development (SSE/WebSocket, reactive programming such as Reactor or WebFlux); understands low-latency and backpressure handling.Familiar with caching (Redis), message queues, and database design; capable of production issue diagnosis and performance optimization. AI/Prompt Engineering Ability (Core) Solid hands-on Prompt engineering experience: proficient in system/role prompt design, few-shot, Chain-of-Thought (CoT), structured output (JSON Schema / constrained decoding), context window management and trimming, multi-turn dialogue state control.LLM application engineering ability: familiar with mainstream LLM APIs and orchestration, skilled in streaming token processing, Function/Tool Calling, multi-model routing/switching, token and cost control, time-to-first-token optimization, timeout/retry/fallback.Able to iterate systematically based on evaluation: can build evaluation and regression mechanisms (automated evaluation, bad-case attribution, A/B comparison, human annotation feedback loop), using data to drive prompt optimization rather than intuition.Understands the capability boundaries and sampling parameters of mainstream LLMs (temperature/top-p, etc.), and the applicable scenarios and trade-offs of embeddings and RAG/fine-tuning.Has real hands-on experience handling LLM hallucination, stability, and content safety issues. Bonus Points Familiar with LLM orchestration/application frameworks (Spring AI, LangChain, etc.).Experience with conversational, educational/teaching, or multi-agent (orchestration, memory, instruction parsing) systems.Experience designing RAG, vector retrieval, or long/short-term memory / context systems.Experience building LLM evaluation platforms or prompt versioning/experimentation platforms.Familiar with how TTS/ASR voice pipelines coordinate and optimize latency in real-time dialogue scenarios.Keeps up with cutting-edge LLM developments and can apply new methods in practice.