About The Role

We are seeking an experienced AI Engineer to manage the design, deployment, and optimization of production-grade language model systems. This role involves building applications using both commercial LLM APIs and self-hosted open-source models, implementing RAG pipelines, and creating end-to-end LLM workflows. The ideal candidate combines practical experience integrating LLM APIs with technical expertise in deploying and optimizing local models.

Key Responsibilities

Design and implement high-throughput, low-latency serving architectures for LLM applications
Build and maintain RAG pipelines and end-to-end LLM workflows
Integrate and optimize commercial LLM APIs (OpenAI, Anthropic, Google, etc.) into production systems
Develop prompt engineering techniques and prompt management systems
Deploy and serve local open-source language models for specific use cases
Optimize local model inference performance through efficient serving frameworks
Fine-tune models to improve performance on domain-specific tasks (
Monitor and troubleshoot production LLM systems to ensure reliability
Research and experiment with emerging models and techniques to improve system capabilities
Document architectures, best practices, and technical decisions
Collaborate with engineering teams to integrate LLM capabilities into products
Communicate technical terms and recommendations to stakeholders
Build evaluation frameworks to measure model quality, latency, cost, and user satisfaction
Design intelligent routing and fallback strategies across multiple LLM providers
Scale LLM services to handle production workloads efficiently
Implement caching, batching, and request optimization strategies for both APIs and local models

Experience

Required Qualifications

Solid 2+ years focused on LLM applications or chatbot development (Candidates with more experience will be considered for a senior role.)
Proven track record of building production LLM applications
Experience integrating and optimizing commercial LLM APIs
Hands-on experience deploying local models in production environments

Technical Skills

Strong Python programming with emphasis on async/await patterns and production-quality code
Deep understanding of transformer architectures and LLM fundamentals
Experience with LLM APIs (OpenAI, Anthropic Claude, Google Gemini, or similar)
Familiarity with local open-source models (Qwen, Llama, Mistral, or similar)
Experience with RAG implementation using LlamaIndex, LangChain or similar frameworks
Proficiency with FastAPI for building high-performance APIs
Experience with vector databases (Pinecone, Weaviate, Chroma, Milvus, or similar)
Working knowledge of MongoDB or other NoSQL databases
Experience with Docker containerization and deployment
Good to have hands-on fine-tuning experience (LoRA, QLoRA, full fine-tuning)
Familiarity with local model serving frameworks (vLLM, TGI, or similar)
Familiarity with LLM workflow tracing and observability frameworks (MLflow, Phoenix, Langfuse, or similar)
Familiarity with Hugging Face ecosystem and transformer libraries
Experience with cloud platforms (AWS, GCP, or Azure)
Proficiency with Git/GitHub and version control workflows

Domain Knowledge

Understanding of prompt engineering and optimization techniques
Knowledge of LLM evaluation metrics and benchmarking methodologies
Experience with cost optimization for LLM applications
Familiarity with distributed computing and scaling strategies
Understanding of LLM inference optimization (quantization, batching, caching)

Preferred Qualifications

Understanding of digital human technologies and multimodal applications
Knowledge of MLOps practices and CI/CD for ML systems
Experience with Kubernetes for container orchestration
Experience with streaming inference and real-time applications
Background in function calling and tool use with LLMs
Familiarity with RLHF (Reinforcement Learning from Human Feedback)
Experience with model distillation and knowledge compression
Understanding of distributed training and GPU optimization
Experience with multi-agent systems and LLM orchestration

Soft Skills & Communication

Excellent English communication skills (written and verbal)
Excellent Chinese reading skill
Ability to explain complex technical concepts to both technical and non-technical audiences
Strong problem-solving and analytical thinking capabilities
Self-motivated with ability to work independently and drive projects to completion
Collaborative team player who thrives in fast-paced environments
Passion for staying current with rapidly evolving LLM technologies
Ability to balance research experimentation with production reliability requirements

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