Deep explanation
System design: a financial research assistant with Transformers
Senior system design: a self-hosted financial research assistant built with Hugging Face Transformers, sentence-transformers retrieval, cross-encoder reranking, PEFT fine-tuning, pinned revisions, safetensors, a dedicated inference server, and citation-checked answers.
Quick answer
I'd build a RAG system entirely inside our network: documents are parsed and chunked, embedded with a sentence-transformers model, reranked with a cross-encoder, and answered by an instruction-tuned open-weights LLM served from a dedicated inference server on our GPUs. Transformers is the toolkit for loading, evaluating, and fine-tuning these models (with PEFT/LoRA where it helps), with every model pinned to a commit and loaded from safetensors. Answers must cite chunk IDs, and numbers are checked against the source text in code before an analyst sees them.
The interview question
Your firm cannot send research notes and deal documents to external APIs, so the financial research assistant must run on open-weights models you host yourself using the Hugging Face Transformers ecosystem. Design the system: which models you need (embedding, reranking, extraction, generation), how you fine-tune and serve them, how you keep answers cited and numbers exact, and how you handle security, scaling, evaluation, and rollout.
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