Senior AI Product Manager

Overview

In this role you will own the Covenants AI product area, shaping AI-powered workflows for legal and financial analysis in debt markets. You’ll collaborate with engineering, legal analysts, and customers to define strategy, drive high-impact features, and ensure production-grade AI outputs. The role blends strategy, discovery, and hands-on delivery in a fast-growing, AI-native company. You’ll help advance a platform used by top banks and law firms, expanding our leadership in legal AI for debt markets.

Pay / Benefits

• Competitive Salary
• Pension (matching)
• Private Medical Insurance
• Hybrid working
• 25 holiday days
• 1 month paid sabbatical after 5 years

Responsibilities

• Define and champion the Covenants squad product vision and strategy in collaboration with engineering, analysts, and customers
• Synthesize insights from SMEs, interviews, and data to articulate clear problem statements and root causes
• Discover user needs across credit analysts, portfolio managers, restructuring advisors, and law firms to inform roadmaps
• Prioritize initiatives to achieve measurable outcomes and align teams around key goals
• Define and manage a process for continuous model evaluation and improvement emphasizing legal-grade accuracy
• Lead collaborative squad dynamics with clear goals and context to enable high performance
• Communicate strategy and progress to stakeholders, translating complex AI concepts for non-technical audiences
• Champion responsible AI practices: transparency, bias monitoring, and compliance with client expectations
• Own the roadmap for AI-driven legal document extraction and covenant analysis, ensuring high precision for professional use
• Partner with engineering to establish evaluation frameworks for AI outputs in legal contexts

Key requirements

• 5+ years of product management experience in FinTech or B2B SaaS
• At least 2 years focused on ML or GenAI products
• Working knowledge of RAG, embeddings, prompt engineering, and LLM evaluation
• Experience shipping AI products with probabilistic output quality management
• Comfort reading Python notebooks and SQL to interrogate models and usage data
• Track record of balancing ML research/exploration with production delivery constraints
• Collaborative leadership with strong stakeholder management
• Clear, user-centric communication of AI capabilities to clients and internal teams
• Ability to work in fast-paced, ambiguous environments and define strategy from scratch

• Collaborative leadership
• Stakeholder management
• Prioritisation and decision-making
• RAG architectures
• Embeddings
• Prompt engineering

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