Senior AI Engineer – Legal AI

Overview

In this role you will build AI-powered systems that convert complex legal and financial documents into structured data. You will work within the Data & AI team to deploy scalable solutions across debt and legal workflows, collaborating with researchers and engineers to drive adoption of AI at 9fin. You’ll own end-to-end model development, from prototyping to production, with a focus on precision extraction and reliability. This is an opportunity to shape the data backbone of a fast-growing platform used by top financial institutions. You’ll join a mission-driven environment that values experimentation, collaboration, and continuous learning.

Pay / Benefits

• Competitive salary
• Equity
• Pension with employer matching
• Private medical insurance
• Hybrid working
• Annual sabbatical after 5 years

Responsibilities

• Design and build generative AI applications for complex financial/legal workflows
• Drive end-to-end model development lifecycle with reproducible research and deployment plans
• Collaborate across teams to share ideas and mentor teammates
• Translate problems into scoped bets in a fast-paced internal startup environment
• Learn and apply advanced research approaches, iterating with a fail-fast mindset
• Lead and push AI adoption across 9fin

Key requirements

• Legal Document Intelligence experience with complex documents (bond offerings, credit agreements, indentures, filings)
• Expertise in extracting structured data from long, complex documents (terms, covenants, parties, dates, ratios)
• Strong document understanding & NLP (structure, sections, tables, cross-references)
• Experience applying LLMs to high-precision extraction with schema-driven methods
• Data quality, verification, and reconciliation workflow design
• Training data creation, annotation frameworks, synthetic data generation, evaluation sets
• Retrieval/Context Engineering for long-document reasoning
• Financial & legal domain knowledge (leveraged finance, debt capital markets, covenant packages)
• Production ML engineering (pipelines, model serving, monitoring)
• Proficiency in Python for production delivery
• Experience across the full model lifecycle (experimentation, training, testing, monitoring, deployment)
• Product-focused mindset and understanding of end-user impact
• Knowledge of AWS AI infrastructure

• Collaborative and cross-functional teamwork
• Proactive idea sharing and mentorship
• User-centered product mindset
• Python (production ready)
• NLP and document understanding
• LLMs for structured data extraction

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