AI Engineer at 9fin
At a glance
- Company
- 9fin
- Location
- London, GB, United Kingdom
- Work arrangement
- On-site
- Employment type
- Full-time
- Experience
- 5–10 years
- Working hours
- 40 hours
- Visa sponsorship
- Offered, according to the listing
- Posted
- Added to The Stack
✨ The Stack take on this 9fin job
- 🏢 9fin has 2 jobs here, 2 with visa sponsorship see all
- 📊 10 AI Engineer jobs in the United Kingdom in the last 60 days see them
- 🛂 Skilled Worker visa. Standard minimum salary: £41,700 a year guide
- 💡 No salary shown. Ask 9fin if it is above the minimum.
Updated 2026-11-05, from our data and official sources.
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About the role
Responsibilities:
- Design and build generative AI applications for complex financial and legal workflows using state-of-the-art models.
- Lead end-to-end model development: experimentation, training, evaluation, reproducible research, deployment, and monitoring.
- Implement information retrieval pipelines: chunking, embedding models, and re-ranker training to improve search and retrieval.
- Build and integrate entity/relationship extraction, knowledge graph components, and GraphRAG solutions.
- Develop recommendation and ranking systems (personalisation, learning-to-rank, user modelling) for product features.
- Collaborate cross-functionally with product, engineering, and research teams; translate problems into scoped internal initiatives.
- Mentor peers, share findings, and promote AI adoption across the organisation.
Requirements:
- Strong production-grade Python experience and familiarity with ML engineering best practices.
- Proven experience with information retrieval, embeddings, and training re-rankers.
- Experience building or integrating entity extraction/resolution systems and knowledge graph solutions.
- Background in recommendation/ranking systems, personalization, or learning-to-rank methods.
- Hands-on experience across the model lifecycle: experimentation, training, testing, deployment, and monitoring.
- Practical knowledge of cloud/AI infrastructure (e.g., AWS), containerisation, and reproducible workflows.
- Good communication skills and ability to work in a fast-paced, cross-functional environment.
Responsibilities
Design and build generative AI applications tailored to complex financial and legal workflows; 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 share diverse ideas and elevate teammates; push AI adoption across 9fin.
Requirements
Strong Python production experience; deep expertise in Information Retrieval, embedding models, training re-rankers; Entity/relationship extraction; Knowledge Graphs and GraphRAG; experience with recommendation systems, personalization, learning-to-rank, and user modelling; knowledge of AWS AI infrastructure; experience across the full model lifecycle (experimentation, training, testing, monitoring, deployment).
Key skills
- Python — in 1200 other listings on The Stack
- Information Retrieval — in 5 other listings on The Stack
- Embedding models — in 1 other listing on The Stack
- Training re-rankers — not in any other listing on The Stack
- Entity/relationship extraction — not in any other listing on The Stack
- Knowledge Graphs — in 2 other listings on The Stack
- GraphRAG — not in any other listing on The Stack
- Recommendation systems — in 16 other listings on The Stack
- Personalisation
- Learning-to-rank
- User modelling
- Tabular ML
On The Stack (as of 2026-11-05)
Frequently asked questions about 9fin
Is this AI Engineer role still open?
It was posted on 2026-11-04 and is listed as active on The Stack. Confirm on 9fin's application page before applying.
Does 9fin offer visa sponsorship?
This listing is marked as offering visa sponsorship. Eligibility rules are set by the employer, so check the details on 9fin's site.
Where is this role located?
The listing gives the location as London, GB, United Kingdom. It is marked on-site.
What skills does this role ask for?
The listing names: Python, Information Retrieval, Embedding models, Training re-rankers, Entity/relationship extraction, Knowledge Graphs, GraphRAG, Recommendation systems, Personalisation, Learning-to-rank, User modelling, Tabular ML.