Senior Product Manager - AI Platform (FinTech)

About the role

We are seeking a strategic and technically fluent Senior Product Manager to own the AI platform layer that underpins our vertical SaaS offering for the lending industry. You are responsible for the foundational capabilities-model serving infrastructure, data pipelines, APIs, SDKs, and developer tooling-that enable both internal engineering teams and, in select cases, external integrators to build AI-powered experiences on top of our platform. You have hands-on familiarity with how production of AI systems are built and operated. You can hold a meaningful technical conversation with an ML engineer about inference latency and embedding strategies - and translate those tradeoffs into crisp product decisions. You have shipped platform capabilities, not just features, and you understand the difference: versioned APIs, backward compatibility, SLA contracts, and developer experience are as important to you as end-user outcomes. Candidates with prior experience building software for consumer lending or mortgage-LOS platforms, automated underwriting, document intelligence, or decisioning engines-will move to the top of the queue. Deep domain familiarity shortens the runway to credibility with both engineering and go-to-market teams.

Responsibilities

• AI Product Strategy and Roadmap
• Own the end-to-end product strategy and roadmap for the AI platform layer.
• Partner with executive leadership to align AI initiatives with company-wide product vision and revenue goals.
• Build business cases justifying R&D investment based on expected benefits.
• Partner with principal engineers and ML infrastructure leads to make informed build-vs-buy-vs-partner decisions on foundational AI capabilities
• Establish and govern platform-level standards: API versioning policies, model lifecycle management, prompt versioning, and observability requirements
• Stay updated with the latest trends and advancements in AI and ML, to identify opportunities for innovation and incorporate relevant insights into product strategy and development .
• Developer Experience and Internal Platform Customers
• Treat internal R&D teams as your primary customers. Conduct structured discovery with feature teams to understand their AI integration pain points, latency requirements, and data access needs.
• Define and own the developer experience for consuming the AI platform: API contracts, SDK design, documentation standards, sandbox environments, and onboarding flows.
• Establish a platform roadmap governance process: intake, prioritization, and communication of platform changes to dependent teams.
• Build feedback loops with consuming teams post-release to detect friction, integration failures, and unmet capability needs early.
• AI Governance, Compliance, and Risk
• Establish monitoring and observability standards: model drift detection, confidence thresholds, input distribution shifts, and alerting policies
• Translate regulatory requirements for AI use in lending (FCRA, ECOA, HMDA, OCC SR 11-7 model risk management) into concrete platform requirements: explainability APIs, audit logging, adverse action reason codes, and human-in-the-loop override mechanisms.
• Partner with information security to define data residency, encryption-at-rest/in-transit requirements, and PII handling policies for AI data flows.
• Maintain a clear capability matrix of which AI features are permissible for which customer tiers, regulatory environments, and data sensitivity levels.
• Measurement, Reliability, and Platform Health
• Define and own platform-level SLOs: inference availability, P99 latency, pipeline throughput, and data freshness.
• Build platform health dashboards and escalation playbooks for AI service degradation-distinct from application-layer monitoring.
• Track platform adoption metrics: number of consuming teams, API call volumes, feature flag usage, and time-to-integrate for new consumers.
• Hold regular platform reviews with engineering leadership to surface technical debt, capacity constraints, and architectural risks before they affect downstream feature teams.
• Align platform metrics with those of the AI-based application products; collaborate with application Product Managers to ensure alignment.

Requirements

• Product Management
• 5+ years' experience in product management, with proven success designing enterprise AI/ML products in a SaaS B2B environment.
• At least 3 years in a platform, infrastructure, or developer tools
• Experience conducting customer/user research, usability testing, and translating insights into product strategy. Proficiency with AI-driven prototyping methods.
• Strong organizational and multi‑tasking abilities, capable of managing multiple projects, priorities, and communication channels in a fast‑paced environment
• Mastery of agile methodologies, processes, artifacts. Understanding exposure to emerging DevAI practices.
• Strong problem-solving skills
• Effective storytelling and presentation abilities
• Excellent collaboration skills withi

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