Senior AI Engineer

About the role

You will design and build generative AI applications tailored to complex financial and legal workflows, leveraging state-of-the-art models to deliver innovative, practical solutions that streamline decision-making and unlock new efficiencies across the platform. You will drive the end-to-end model development lifecycle, leading the team in best practices and ensuring reproducible research alongside well-managed model delivery and deployment in production environments. You will collaborate cross-functionally to share diverse ideas, deeply understand business problems, and elevate and mentor your teammates as they tackle ambitious challenges. You will translate complex problems into well-defined scoped bets within an internal startup style environment that is dynamic, fast-paced, and highly iterative. You will learn and apply groundbreaking research and approaches in advanced topics, iterating improvements with a fail-fast mentality while rigorously validating outcomes. You will be a proactive sharer of compelling ideas and work to the rest of the team and organization, helping lead and push the adoption of AI throughout 9fin on a daily basis.

Key facts

What you'll do

• Architect and implement scalable AI systems that ingest, process, and extract value from the world's largest debt markets datasets, ensuring robustness and performance at every stage.
• Lead the design of retrieval-augmented generation pipelines, optimizing chunking strategies, embedding model selection, and re-ranking techniques to maximize relevance and precision in financial contexts.
• Pioneer the application of entity and relationship extraction and resolution methods, constructing and reasoning over knowledge graphs to surface critical insights from unstructured documents.
• Build and refine recommendation systems and ranking models that drive personalization, learning-to-rank approaches, and user modeling for diverse professional workflows.
• Advance tabular machine learning capabilities, applying feature engineering and structured data modeling techniques such as gradient-boosted decision trees to solve risk and pricing problems.
• Develop statistical and predictive modeling solutions, including supervised learning, anomaly detection, and temporal modeling, to support real-time decision-making and risk management.
• Conduct deep analysis of the generative AI landscape and agentic frameworks, integrating components that enable autonomous, tool-using behaviors in complex financial scenarios.
• Own the full model lifecycle, from experimentation and training through testing, monitoring, and deployment, while championing a data-centric culture that values rigorous evaluation and continuous improvement.
• Partner closely with product, engineering, and research teams to uncover problems, align on outcomes, and translate business requirements into technically elegant and impactful AI solutions.
• Mentor engineers and researchers, sharing best practices and elevating the technical capabilities of the organization as AI adoption accelerates across all product lines.

Requirements

• Strong proficiency in Python for production-ready delivery, with a focus on maintainable code, testing, and scalable system design.
• You must have deep expertise in at least one of the following areas and familiarity with others: Information Retrieval, including chunking strategies, (finetuning) embedding models, and training re-rankers.
• You must have deep expertise in at least one of the following areas and familiarity with others: Entity and relationship extraction and resolution.
• You must have deep expertise in at least one of the following areas and familiarity with others: Knowledge Graphs, including building and inference, and graphRAG approaches.
• You must have deep expertise in at least one of the following areas and familiarity with others: Recommendation systems and ranking, such as personalization, learning-to-rank, feed ranking, and user modeling.
• You must have deep expertise in at least one of the following areas and familiarity with others: Tabular machine learning, including feature engineering and structured data modeling with methods such as gradient-boosted decision trees.
• You must have deep expertise in at least one of the following areas and familiarity with others: Statistical and predictive modeling, including supervised machine learning, anomaly detection, and temporal modeling techniques.
• You must have deep expertise in at least one of the following areas and familiarity with others: Generative AI landscape and agentic frameworks, with an understanding of how to implement agentic workflows and tool use.
• You have extensive experience working in the full model lifecycle, including experimentation, training, testing, monitoring, and deployment, and you are data-centric with a strong appreciation for evaluation methodologies.
• You have a strong product focus and a demonstrated desire to understand the end-to-end impact of your solutions for real users in demanding financial environments.
• You have good knowledge of AWS's AI infrastructure and related services used to support large-scale, secure, and compliant machine learning workloads.
• You are comfortable operating in fast-paced, ambiguous environments and can make high-quality decisions with incomplete information while communicating clearly and persuasively.
• You are a collaborative leader who enjoys mentoring peers, contributing to technical strategy, and helping shape the data and AI culture of a rapidly growing global organization.
• You have experience partnering with cross-functional stakeholders such as product managers, researchers, and infrastructure teams to align on goals and deliver reliable, scalable solutions.
• You are committed to maintaining best practices around reproducibility, experiment tracking, and model governance to ensure that AI systems remain robust, auditable, and aligned with business objectives.

Nice to have

• Preferred experience includes work with large language models, transformer architectures, and related tooling for generation and inference in production settings.
• Preferred experience building and deploying retrieval-augmented generation systems in regulated, data-sensitive industries.
• Preferred experience contributing to open-source AI projects or publishing research in relevant domains.
• Preferred experience with advanced agentic frameworks, tool use, and autonomous system design in real-world applications.

Practical notes

This role is full-time based in London. Hybrid working model is available to allow flexibility in how, where, and when you perform your best work. You may work abroad for up to 3 months in any 12-month period. After 5 years of service, you are eligible for 1 month of paid sabbatical. The position does not include compensation details in the public listing.