AI Engineer (Managed Services)

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/ 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

• Bachelors degree or above in Computer Science Artificial Intelligence Machine Learning or related technical field. Masters 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 () 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.