We are looking for a LLM Engineer/Researcher to continue to fine-tune and train our LLM models.
Responsibilities
- Train and Fine-tune foundational LLM models (e.g. PEFT, Lora, QLora, latest research techniques)
- Build and maintain LLM applications and infrastructure to meet business needs
- Design LLM inference infrastructure to scalably deploy LLMs within infrastructural constraints
- Research and utilise best of class tools within LLM ecosystem (e.g. Vector databases, LlamaIndex, etc)
- Keep up with latest research around LLMs (e.g. sparse models, hardware-specific LLMs)
- Research and keep up with latest use-cases of LLMs (e.g. RAG, Agents, etc)
- Collaborate closely with LLM research teams to participate in foundation model research, specifically for training productivity-related LLMs.
Requirements
- Experience with LLMs, including popular foundational models like Llama2 and MPT
- Experience with training and fine-tuning foundational LLM models
- Experience with quantisation techniques, including llama.cpp, GPTQ, etc
- Experience with LLM related development, e.g. Llamaindex, Langchain, Vector DBs, Prompt Engineering etc
- [Plus but not required] Experience running LLMs in production (e.g. Triton Inference Server, etc)
