We are looking for a highly skilled AI Engineer specializing in Large Language Models (LLMs) and Agentic AI. You will architect, build, and deploy production-grade LLM applications — from intelligent knowledge bases and RAG systems to autonomous multi-agent workflows. You will work hands-on with open-source Chinese and international LLMs (DeepSeek, Qwen, Kimi, etc), implementing everything from model deployment and inference optimization to prompt engineering and agent orchestration. This is a builder role for someone who thrives at the intersection of research and engineering.
KEY RESPONSIBILITIES
LLM Application Development
Design and develop enterprise LLM-powered applications: intelligent Q&A systems, enterprise knowledge base assistants, AI copilots, document analysis tools, and automated customer service agents.
Architect and implement end-to-end RAG (Retrieval-Augmented Generation) systems: document parsing and chunking (recursive, semantic, agentic), embedding generation (BGE, M3E, GTE), vector retrieval (dense + sparse hybrid search), reranking (bge-reranker, Cohere Rerank), and response synthesis with source attribution.
Develop and optimize Prompt Engineering strategies: chain-of-thought, tree-of-thought, few-shot prompting, structured output parsing (JSON mode / Pydantic), prompt templates (LangChain/LangSmith), and prompt version management.
Knowledge in harness engineering, context management in ensuring LLM interactions and or AI agents reliable and deterministic.
AI Agent & Multi-Agent Systems
Design and build AI Agent systems using ReAct, Plan-and-Execute, Reflection, and multi-agent collaboration patterns.
Implement Function Calling and tool-use capabilities, enabling agents to interact with external APIs, databases, and enterprise systems.
Develop multi-agent orchestration using LangGraph, AutoGen, CrewAI, and other agent frameworks to solve complex enterprise tasks through agent collaboration.
Design MCP (Model Context Protocol) integrations for standardized LLM tool interoperability.
Open-Source LLM Deployment & Optimization
Deploy and optimize latest version of open-source Chinese LLMs: DeepSeek, Qwen, and Kimi for on-premise and private cloud environments.
Implement model inference optimization: quantization (GGUF/llama.cpp, GPTQ, AWQ, AutoAWQ, FP8/INT8), KV Cache optimization, continuous batching (vLLM, TensorRT-LLM, TGI, SGLang), speculative decoding, and tensor parallelism for high-throughput serving.
Build and maintain model serving infrastructure using vLLM, TensorRT-LLM, Text Generation Inference (TGI), Ollama, Xinference, and SGLang; configure GPU resource scheduling with Kubernetes + GPU operators. AI gateway tools for routing, model tracking and load balancing such as TrueFoundry, Kubeflow, LiteLLM or Ray for heavy deep learning.
Model Fine-Tuning & Customization
Implement efficient fine-tuning pipelines using LoRA, QLoRA, DoRA, and full-parameter fine-tuning on proprietary domain-specific datasets.
Prepare and curate instruction-following datasets, RLHF/RLAIF datasets, and evaluation benchmarks for domain adaptation.
Evaluate fine-tuned models using automated benchmarks and LLM-as-a-Judge methodologies.
Evaluation & Production Operations
Build and maintain LLM evaluation frameworks: LLM-as-a-Judge, RAGAS, DeepEval, ARES, and custom task-specific metrics for continuous quality monitoring.
Implement production monitoring for LLM systems: output quality tracking, latency/throughput metrics, cost monitoring, drift detection, and guardrail compliance.
Design A/B testing frameworks for model comparison and prompt iteration.
Implement LLM security guardrails: input/output filtering, PII detection, prompt injection defense, content moderation, and safety alignment.
Research & Technical Leadership
Track frontier AI research and evaluate emerging technologies (new model architectures, training techniques, inference methods) for enterprise adoption.
Contribute to internal knowledge sharing: tech talks, documentation, and best-practice guides on LLM development.
REQUIRED QUALIFICATIONS
Bachelor's degree or above in Computer Science, Artificial Intelligence, Machine Learning, or related technical field. Master's or PhD in AI/ML preferred.
2+ years of professional experience in AI/ML engineering with demonstrated production deployment of LLM-based systems at scale.
Deep understanding of Transformer architecture, attention mechanisms (MHA, GQA, MQA), and LLM pre-training / fine-tuning / inference paradigms.
Expert proficiency in LLM application frameworks: LangChain, LlamaIndex, Haystack, or equivalent production-grade tools.
Hands-on experience with RAG system development: vector databases (Milvus, ChromaDB, Qdrant, Weaviate, Pinecone, pgvector), embedding models (BGE, M3E, GTE, OpenAI, Cohere), reranking (bge-reranker, Cohere Rerank, cross-encoders), and advanced retrieval techniques (hybrid search, query expansion, HyDE).
Practical experience deploying and tuning open-source Chinese LLMs: DeepSeek, Qwen, Kimi , or international models (Llama 3.x, Mistral, Mixtral, Gemma, Phi).
Strong experience with model deployment and serving infrastructure: vLLM, TensorRT-LLM, TGI, Ollama, Xinference, SGLang; GPU resource scheduling (Kubernetes + GPU operators).
Proficiency in model quantization and inference optimization: GGUF (llama.cpp), GPTQ, AWQ, AutoAWQ, FP8/INT8; knowledge of KV Cache optimization and memory-efficient attention (FlashAttention, FlashInfer, PageAttention).
Solid programming skills in Python; experience with PyTorch, TensorFlow, or JAX; familiarity with FastAPI/Flask for building LLM API services.
Experience with LLM evaluation methodologies, A/B testing frameworks, and production monitoring of AI systems.
PREFERRED QUALIFICATIONS
Experience with agent frameworks: LangGraph, AutoGen, CrewAI, OpenAI Assistants API, and multi-agent orchestration patterns.
Familiarity with MCP (Model Context Protocol), OpenAI API specification, and multi-modal LLM capabilities (vision, audio).
Experience with prompt optimization tools: DSPy, PromptLayer, LangSmith for systematic prompt engineering.
Knowledge of model distillation and efficient transfer learning from large teacher models to smaller student models.
Contributions to open-source AI projects or publications in NLP/LLM research venues.
Experience with cloud GPU providers and cost optimization for LLM inference at scale.
Access to high-performance GPU computing resources for model development and experimentation.
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