Senior AI Product Manager, 9fin

• 5+ years of product management experience, preferably in FinTech or B2B SaaS, with at least 2 years focused on ML or GenAI products
• Working knowledge of RAG architectures, embeddings, prompt engineering, and LLM evaluation methods (not just "AI concepts" at a high level)
• Experience shipping AI products where output quality is probabilistic: you know how to define "good enough" and iterate from there
• Comfortable reading Python notebooks and SQL to interrogate model outputs and usage data
• Track record of shipping AI products where you managed the tension between ML research/exploration and production delivery constraints
• Collaborative leadership style with strong stakeholder management; you build relationships that empower your team to achieve outcomes
• User-centric communication skills: you can explain AI capabilities and limitations to clients and internal stakeholders without overselling or underselling
• You thrive in fast-paced, ambiguous environments and are energised by the challenge of building something new
• If you’re excited about this role but your experience doesn’t perfectly align with the job description, we encourage you to apply anyway. You might just be who we’re looking for — either for this role, or perhaps another

Desirable

• Our AI product area is growing. We already have two Product Managers working across conversational AI, agentic workflows, and domain-specific automation, and we need a third to expand our capacity as the scope of AI at 9fin accelerates
• Backed by over $50M in funding (Series B), we are scaling rapidly across product, engineering, and go-to-market. AI is not a bolt-on feature at 9fin; it is the core differentiator for the next phase of the company
• You will join an established AI PM team and take ownership of a product area within our AI surface
• This could span new AI-powered features for credit analysts, workflow automation for legal and financial analysis, or expanding our conversational AI into new domains
• The team has already built evaluation infrastructure and quality frameworks; you will use and contribute to these rather than build them from scratch
• You will work within our product organization (eight squads across AI, data, and domain verticals) and partner closely with ML engineers, data scientists, domain experts, and designers
• This is a high-autonomy role with real scope to shape what 9fin's AI products can do for our clients
• Collaboratively create and champion a clear product vision and strategy for your area, with a focus on building and evolving our AI products
• Synthesize qualitative and quantitative insights into problem statements that identify root causes, not just symptoms
• Shape the next generation of agentic AI products: multi-step reasoning workflows, tool orchestration, and autonomous task completion for financial professionals
• Demonstrate a working understanding of the challenges specific to building with LLMs, including managing hallucinations, evaluating output quality, and designing for non-deterministic systems
• Use confidence-building methods (prototypes, user research, data analysis) to understand how users will interact with AI-powered products before committing to full builds
• Use the team's AI evaluation framework (LLM-as-judge protocols, quality thresholds, SME eval cycles) to validate and improve the products you ship
• Contribute to the data strategy for your product area: annotation quality, ground truth curation, and feedback loops that improve model performance over time
• Proactively identify and track metrics that measure client and business benefit, working with the team on continuous model evaluation and improvement
• Create a collaborative environment for your squad, providing wider context and clear goals so everyone can do their best work
• Work with data scientists and ML engineers to bridge the gap between business needs and the technical capabilities of AI models
• Communicate strategy, initiatives, and progress transparently across the organisation
• Translate complex AI concepts, capabilities, and limitations into language that non-technical stakeholders can act on
• Champion responsible AI practices: transparency in model outputs, bias monitoring, and compliance with client expectations around AI-generated content