AI Product Manager – Commercial Lending

Brillio; New York (NY); On-site; USD 140,000 - 200,000; Full time

Job summary

Brillio seeks an experienced AI Product Manager with strong commercial lending domain expertise to identify, design, and deliver AI-enabled products across the lending lifecycle.

The role intersects commercial lending, product management, data and AI, collaborating with relationship managers, credit teams, risk and compliance, data scientists, designers and engineers to scale AI solutions that improve decision-making, productivity and customer experience.

Qualifications

• The candidate has 7+ years in product management, banking transformation or financial-services tech.
• 3+ years of direct commercial lending experience or delivering products for lending organizations.
• Experience turning business problems into product requirements, epics and user stories.

Responsibilities

• Own product strategy and roadmap for AI-enabled capabilities across the lending lifecycle.
• Translate lending pain points into well-defined AI product opportunities.
• Collaborate with AI/ML engineers and data scientists to define solutions.
• Define evaluation frameworks including accuracy, latency and business outcomes.
• Lead adoption from discovery through MVP, production deployment and scale.

Skills

Product management

AI product experience

Commercial lending

Stakeholder management

Job description

We are looking for an experienced AI Product Manager with strong commercial lending domain expertise to identify, design, and deliver AI-enabled products that transform the commercial lending lifecycle.

The role sits at the intersection of commercial lending, product management, data and AI. You will work closely with relationship managers, credit and underwriting teams, lending operations, risk and compliance stakeholders, data scientists, designers and engineers to identify high-value opportunities and translate them into scalable AI products.

The ideal candidate understands both how commercial lending works end-to-end and how modern AI—including generative AI, agentic workflows, machine learning and document intelligence—can improve decision-making, productivity, customer experience and operational efficiency.

Key Responsibilities

Product Strategy & Opportunity Identification

• Own product strategy and roadmap for AI-enabled capabilities across the commercial lending lifecycle.
• Identify and prioritize opportunities based on business value, feasibility, risk and user impact.
• Develop business cases and define measurable outcomes including cycle-time reduction, productivity improvement, risk reduction, revenue impact and customer experience.
• Translate lending pain points into well-defined AI product opportunities rather than treating AI as a technology-first initiative.
• Partner with business and technology leaders to move successful AI experiments from pilot to scaled production products.

Identify and develop AI solutions across areas such as:

• Prospecting and relationship-manager intelligence
• Loan origination and application intake
• Financial spreading and borrower financial analysis
• Credit assessment and underwriting
• Credit memo preparation and review
• Covenant extraction and monitoring
• Loan documentation and document intelligence
• Collateral analysis
• Due diligence and exception management
• Portfolio monitoring and early-warning signals
• Annual reviews and credit renewals
• Loan servicing and operations
• Policy, procedure and credit-guideline intelligence

Understand dependencies across relationship management, credit, underwriting, operations, risk, compliance and servicing when designing solutions.

AI Product Management

• Translate business problems into product requirements, user journeys, epics, stories and acceptance criteria.
• Determine when to apply traditional automation, machine learning, generative AI, agentic AI or combinations of these approaches.
• Work with AI/ML engineers, data scientists and architects to define solution approaches and evaluate technical feasibility.
• Design human-in-the-loop workflows appropriate for regulated lending decisions.
• Define evaluation frameworks for AI products, including accuracy, hallucination risk, explainability, latency, cost and business outcomes.
• Establish feedback loops to continuously improve AI product performance.
• Understand the implications of model choice, retrieval-augmented generation, enterprise knowledge, data quality and AI orchestration on product design.
• Ensure AI products operate within commercial lending risk-management and regulatory requirements.
• Partner with credit risk, model risk, compliance, legal, information security and data governance teams.
• Incorporate traceability, explainability, auditability and human oversight into product requirements.
• Clearly distinguish between AI that assists a lending professional and AI involved in credit or other consequential decision-making.
• Define appropriate controls, escalation mechanisms and monitoring for AI-enabled workflows.

Product Delivery & Adoption

• Lead products from discovery and prototype through MVP, production deployment and scale.
• Conduct user research with relationship managers, underwriters, credit officers and lending operations teams.
• Establish product KPIs and measure adoption, business value and user satisfaction.
• Drive change management and adoption by embedding AI capabilities into existing lending workflows rather than creating disconnected tools.
• Partner with engineering teams using agile product-development practices.

Required Experience

• 7+ years of product management, banking transformation or financial-services technology experience, with meaningful experience owning digital or data products.
• 3+ years of direct commercial lending experience or significant experience delivering products for commercial lending organizations.
• Strong understanding of the commercial lending lifecycle from origination through underwriting, closing, servicing and portfolio management.
• Working knowledge of areas such as financial spreading, credit analysis, credit memos, covenants, collateral, loan documentation and portfolio monitoring.
• Experience working with lending platforms, banking data and complex enterprise workflows.
• Demonstrated experience taking products from problem discovery through production and adoption.
• Experience partnering with business, engineering, design, data and risk/control functions.

Candidates should have practical experience delivering products using one or more of the following:

• AI agents and workflow automation
• Retrieval-Augmented Generation (RAG)
• Machine learning and predictive analytics
• Enterprise search and knowledge management
• APIs and enterprise data integration

Deep hands-on model development is not required, but the candidate must be technically fluent enough to make product decisions and effectively work with AI architects, engineers and data scientists.

Preferred Qualifications

• Experience within commercial banking, corporate banking, middle-market lending or specialty lending.
• Experience with commercial loan-origination or servicing platforms.
• Familiarity with banking regulatory, model-risk and responsible-AI requirements.
• Experience delivering AI products within a highly regulated enterprise.
• Experience modernizing lending processes that depend heavily on documents, unstructured data and manual analysis.
• Experience defining ROI and value realization for AI initiatives.

What Success Looks Like

Within the first 6–12 months, this person should be able to:

• Develop a clear map of the commercial lending journey and its highest-value AI opportunities.
• Build a prioritized AI product roadmap tied to measurable business outcomes.
• Launch initial AI products with strong adoption among lending professionals.
• Demonstrate measurable improvements in areas such as underwriting productivity, credit-review cycle time, operational effort or portfolio insight.
• Establish a repeatable approach for taking commercial lending AI use cases from experimentation to production.
• Build credibility with both senior commercial lending stakeholders and AI/engineering teams.