Senior Product Manager - AI Platform

MeridianLink

1d•$94,667 - $161,400

About The Position

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.

Requirements

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 within and across teams
Ability to give and receive constructive design feedback
Awareness of industry trends, emerging technologies, and best practices in AI product design
Demonstrated track record of taking AI features from concept to production—including model integration, data contracts, and post-launch monitoring
Experience with AI/ML concepts, LLMs, MCPs, GenAI platforms, API integration
Familiarity with responsible AI principles, model interpretability, bias mitigation, and quality/accuracy metrics required for production grade AI systems.
Experience collaborating with Data Science and Engineering teams to define training data needs, evaluate model performance, and implement iterative feedback loops.
Proven track record shipping AI or ML capabilities into production: you have written PRDs that specify inference APIs, data schemas, latency budgets, model versioning strategies, and observability requirements.
Sufficient technical depth to participate in architecture discussions with Engineering.
Hands-on familiarity with at least one modern AI/ML stack, vector databases, and model serving infrastructure.
Experience defining API contracts and SDK developer experiences—including versioning strategies, deprecation policies, and changelog communication.
Comfort working with data engineering concepts: ETL/ELT pipelines, feature stores, schema registries, event streaming (Kafka, Kinesis), and data quality frameworks.
Strong written communication skills for technical audiences.
Bachelor’s degree required; MBA preferred. Computer science background (education or other demonstrated proficiency) required.
Experience in vertical SaaS required.

Nice To Haves

Consumer or mortgage lending software experience is a plus

Responsibilities

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.
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.
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.
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.