Build a tool that standardizes LLM fine-tuning experiments across common model families such as LLaMA, Mistral, Phi, and GPT-style models. It would provide experiment setup, dataset/version tracking, reward and preference model evaluation, topology configuration capture, and side-by-side comparison of SFT, PEFT, RLHF, DPO, contrastive learning, and RFT runs.

Multiple companies across AI infrastructure, consumer ML, autonomous vehicles, ecommerce, and research are hiring for advanced LLM fine-tuning and alignment skills. As more teams move from prompt use to model customization, operational tooling for the model lifecycle becomes more valuable.