Context

9fin is a financial data platform using AI to analyse debt capital markets. Following a £50m Series B, the unicorn had scaled to around 350 employees and was entering a phase focused on building user-facing AI products, aiming to double revenue year on year.

The shift required engineers who could translate complex AI systems into products that solved real user problems.

At the same time, the business was scaling quickly. Senior engineering leaders had limited time to run a long, iterative hiring process, despite the strategic importance of these roles.

The Challenge

This sat in a highly competitive part of the market.

The brief called for people with strong engineering capability and familiarity with problem spaces such as document extraction, generative AI, LLMs and user-facing AI systems. As well as being technically strong, 9fin needed people able to think in terms of product and outcomes.

9fin also has a high bar for talent. Bringing in someone who was just “good enough” would have had a knock-on effect on culture, product quality and team standards.

Aura’s Insight

The hiring process was leaning heavily towards engineering capability rather than data science skills. In practice, this was dramatically narrowing the talent pool.

A lot of the strongest candidates were coming from deeper AI and machine learning backgrounds. By limiting the search to identical problem spaces and focusing too tightly on a specific type of engineer, the search was missing a meaningful part of the market.

Working closely with the hiring team, Aura helped to recalibrate the role around a more balanced profile: engineers with strong ML expertise who could take ownership and build products that delivered real user value.

‍The Search

The search expanded into adjacent AI and ML environments where similar technical challenges were being solved – unlocking access to a broader and more relevant talent pool.

Across the European AI and ML talent market, the focus was on candidates who had:

• deep AI and ML expertise
• strong engineering fundamentals
• ownership of complex product problems
• the ability to identify and solve real user needs

The result was a shortlist of highly relevant candidates. Many of these candidates were already in demand from leading AI companies, so timing and positioning were important throughout.

The Outcome

Three  Senior AI Engineers were hired, with two additional candidates progressing to offer stage.

Aura was the only external partner to secure offers for the role.

The search also surfaced a more diverse group of candidates, including several highly qualified female engineers in a market where representation can be limited.

The Impact

The hires have given the team the capacity to push forward on product-facing AI work during a critical phase of growth. 

They’ve helped move existing AI capability closer to the product, supporting the shift towards building features that directly drive revenue.