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
Excited to join us?
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