AI Product Manager
Role Overview
Lead design and delivery of AI-powered products across data ingestion, modeling, and user-facing applications.
Build production-grade AI systems for investment workflows, financial analysis, and decision-making.
Translate problems in credit risk, deal structuring, and financial modeling into scalable AI-driven solutions.
Work across Product, Data Science, and Engineering on agent-based systems, decision-support tools, and financial data platforms.
Key Skills for This Role
Full Job Posting
About the company
Liquidity is an AI-powered private credit firm supporting high-growth companies with growth capital.
The company operates across North America, Europe, APAC, and MENA and uses proprietary decision-science technology.
About the role
Lead design and delivery of AI-powered products across data ingestion, modeling, and user-facing applications.
Build production-grade AI systems for investment workflows, financial analysis, and decision-making.
Translate problems in credit risk, deal structuring, and financial modeling into scalable AI-driven solutions.
Work across Product, Data Science, and Engineering on agent-based systems, decision-support tools, and financial data platforms.
Responsibilities
- Define AI-native experiences, including chat interfaces, agent workflows, automation, and decision-support tools.
- Translate financial and investment problems into scalable AI systems; drive product development across data, model, and application layers.
- Design multi-agent or prompt-based systems for origination, diligence, and monitoring; maintain structured financial data representations.
- Ensure production AI outputs are accurate, explainable, and reliable; collaborate with Data Science, Engineering, and business stakeholders.
- Debug data, model, and user-experience issues; lead AI assistants, financial data mapping, risk scoring, and simulation tools.
- Balance speed and precision when delivering solutions in financial contexts.
Qualifications
- 5–10 years of Product Management experience with strong technical depth.
- Strong LLM knowledge, including prompting, RAG, evaluation, and hallucination mitigation.
- Knowledge of ETL, APIs, structured and unstructured data systems, and machine learning fundamentals, model behavior, and evaluation metrics.
- Financial, analytical, or data-intensive systems experience is strongly preferred.
- Proficiency in SQL or data querying; experience with API integrations and ability to design high-level architecture across data, models, and product layers.
- Strong problem-solving and cross-layer debugging skills; experience collaborating with Data Science and Engineering teams in production.
- Fintech, analytics-platform, or AI-product background is a strong plus.
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