9fin builds a debt-market intelligence platform for credit professionals, and its stack shows an unusually deep in-house AI bet: open-weight models (Llama, Qwen, Mistral, Gemma) running on AWS alongside Python, FastAPI and Pydantic, with MCP now being adopted. Hiring is sales-led and senior-heavy, with a sales enablement function being built from scratch, which points to a push on account expansion rather than pure product build.
9fin is a London-headquartered, privately held company founded in 2016 that provides news, data, analytics and AI tooling for the debt markets. Its coverage spans public, private and distressed credit, with specialties including leveraged finance, structured credit, private credit and debt capital markets. Customers include investment banks, investors, advisors and law firms, and the company reports serving more than 300 financial institutions. It employs 201–500 people and hires across the United Kingdom and the United States.
9fin runs on Python, Flask, FastAPI and PostgreSQL on AWS (Lambda, ECS, RDS), with React and TypeScript on the front end, Snowflake, BigQuery and dbt for data, and open-weight LLMs such as Llama, Qwen, Mistral and Gemma.
Yes. Sales is 9fin's largest hiring area, alongside engineering, data and product, with roles skewed toward senior hires in the United Kingdom and United States.
9fin is building generative AI applications for financial and legal workflows, AI tooling for trading and bank origination, private credit reporting, and a new sales enablement function.
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9fin's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.
