Senior Product Manager - AI Platform

External

Meridianlink · Remote

$104K–$170K/yrFull-timeRemote1w ago

AgileComplianceDocumentationEncryptionInformation SecurityLeadership

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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.

Senior Product Manager - AI Platform

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