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9fin is an AI platform powering global debt markets, the world's largest asset class at over $145 trillion. The company centralizes proprietary credit data, analysis, and workflows for 300+ blue-chip institutions including global banks, asset managers, private equity firms, law firms, and advisors. The business is scaling rapidly with proven product-market fit and strong market pull.
The Data Science team is expanding and seeking an AI Engineer to accelerate AI application across products and teams. You will build large-scale AI-powered systems using machine learning, computer vision, NLP, speech/audio processing, and knowledge/data mining.
Key responsibilities include:
- Design and build generative AI applications for complex financial and legal workflows, leveraging state-of-the-art models to streamline decision-making and unlock efficiencies
- Drive end-to-end model development lifecycle, leading the team in best practices, ensuring reproducible research and well-managed model delivery/deployment
- Collaborate cross-functionally to understand business problems and mentor teammates
- Translate complex problems into well-defined scoped bets in a dynamic, fast-paced startup environment
- Learn and apply groundbreaking research approaches, iterating improvements with a fail-fast mentality
- Proactively share compelling ideas and work to drive AI adoption throughout 9fin
The role offers hybrid flexibility, allowing you to decide how, where, and when you do your best work. Additional benefits include 25 holiday days per year, pension matching up to 7%, private medical insurance, professional development budget, £800 annual AI experimentation budget, and enhanced parental leave.
REQUIREMENTS:
- Strong proficiency in Python for production-ready delivery
- Expert-level knowledge in at least one of the following areas (with familiarity in others ideal):
* Information Retrieval: chunking strategies, embedding models, re-ranker training
* Entity/relationship extraction and resolution
* Knowledge Graphs: building, inference, graphRAG
* Recommendation systems and ranking: personalization, learning-to-rank, feed ranking, user modeling
* Tabular ML: feature engineering, structured data modeling (GBDTs, etc.)
* Statistical & Predictive Modeling: supervised ML, anomaly detection, temporal modeling
* Deep understanding of GenAI landscape and agentic frameworks
- Experience across the full model lifecycle (experimentation, training, testing, monitoring, deployment) with strong evaluation practices
- Strong product focus with desire to understand end-to-end impact for end users
- Good knowledge of AWS AI infrastructure
