LLM Fine-Tuning Engineer (Open-Weight Models / Secure Environments)

Fine-tune and adapt open-weight LLMs for specialized use cases in secure/local environments

Design, run, and compare different post-training approaches, including: supervised fine-tuning (SFT) parameter-efficient fine-tuning (LoRA / QLoRA and related methods) preference tuning approaches such as DPO where appropriate full fine-tuning when justified by the use case

supervised fine-tuning (SFT)

parameter-efficient fine-tuning (LoRA / QLoRA and related methods)

preference tuning approaches such as DPO where appropriate

full fine-tuning when justified by the use case

Build and improve high-quality datasets for training and evaluation

Generate synthetic data and use it responsibly to expand coverage, improve robustness, and accelerate iteration

Assess model quality beyond headline metrics

Work with engineering teams to operationalize tuned models for local or restricted deployments

Collaborate with domain experts to translate real operational needs into measurable model requirements

Strong experience fine-tuning LLMs or adjacent foundation models in production or serious research environments