LLM Engineer: Build Production-Ready Chatbots & RAG

Pantheon Lab Limited; Hong Kong; Vor Ort; HKD 480.000 - 750.000; Vollzeit

Zusammenfassung

Pantheon Lab Limited seeks an experienced AI Engineer to design, deploy and optimize production-grade language model systems. You will build applications using API-based and self-hosted models, implement RAG pipelines, and create end-to-end LLM workflows.

The ideal candidate combines hands-on LLM API integration with expertise in deploying and optimizing local models for practical use cases. Strong Python, FastAPI, and cloud familiarity are expected.

Qualifikationen

• 2+ years building production-grade LLM applications.
• Experience integrating commercial LLM APIs.
• Hands-on experience deploying local models in production.
• Familiarity with prompt engineering and evaluation.

Aufgaben

• Design high-throughput, low-latency serving architectures for LLM apps.
• Build and maintain RAG pipelines and end-to-end workflows.
• Integrate commercial LLM APIs into production systems.
• Develop prompt engineering techniques and management systems.
• Deploy and serve local open-source models for specific use cases.
• Optimize local model inference performance with efficient serving frameworks.
• Fine-tune models to improve domain-specific tasks.
• Monitor and troubleshoot production LLM systems; ensure reliability.
• Document architectures, best practices, and decisions.
• Collaborate with engineering teams to integrate LLM capabilities into products.

Kenntnisse

Python programming

async/await

transformer models

LLM APIs

local models

RAG pipelines

FastAPI

vector DBs

NoSQL

Docker

LoRA/finetuning

vLLM/TGI

Hugging Face

cloud platforms

Git/GitHub

Tools

LangChain

LlamaIndex

Docker

Kubernetes

MongoDB

Pinecone

Weaviate

Milvus

GitHub

Jobbeschreibung

Pantheon Lab Limited seeks an experienced AI Engineer to design, deploy and optimize production-grade language model systems. You will build applications using API-based and self-hosted models, implement RAG pipelines, and create end-to-end LLM workflows.

The ideal candidate combines hands-on LLM API integration with expertise in deploying and optimizing local models for practical use cases. Strong Python, FastAPI, and cloud familiarity are expected.