Senior Product Manager (AI Platform)

Role overview

Join MeridianLink as a Senior Product Manager for the AI Platform layer. You will own the end-to-end product strategy and roadmap for foundational AI capabilities that enable AI-powered experiences across the company’s lending-focused SaaS offering.

Key responsibilities

• Own product strategy and roadmap for the AI platform layer.
• Partner with executive leadership to align platform priorities and platform-level standards.
• Define and govern platform-level standards, including:
  • Model lifecycle management
  • Observability requirements
• Establish a platform roadmap governance process.
• Translate regulatory requirements into concrete platform requirements.
• Work with engineering to make informed decisions about core AI platform capabilities.
• Define and own developer experience (DX), including API/SDK expectations.
• Ship platform capabilities (not only features), with focus on versioned APIs, backward compatibility, and enterprise-grade contracts (e.g., SLA contracts).
• Collaborate with Data Science and Engineering on:
  • Training data needs
  • Model performance evaluation
  • Iterative feedback loops

Requirements

• 5+ years of product management experience.
• Strong understanding of AI/ML concepts and production AI operations (e.g., inference latency, embedding strategies).
• Experience defining API contracts and SDK developer experiences, including:
  • Versioning strategies
  • Deprecation policies
  • Changelog communication
• Experience delivering enterprise B2B SaaS product outcomes.
• Proficiency with Agile methodologies and product artifacts.
• Strong written communication for technical audiences.
• Experience with customer/user research and translating insights into product strategy (including usability testing).
• Technical depth to participate in architecture discussions with engineering.

Nice-to-haves

• Experience building software for consumer lending or mortgage, such as:
  • LOS platforms
  • Automated underwriting
  • Document intelligence
  • Decisioning engines
• Familiarity with responsible AI (interpretability, bias mitigation) and quality/accuracy metrics for production AI.
• Hands-on familiarity with at least one modern AI/ML stack, including vector databases and model serving infrastructure.
• Comfort with data engineering concepts such as ETL/ELT, feature stores, schema registries, and event streaming (Kafka, Kinesis).
• Exposure to emerging DevAI practices and AI-driven prototyping methods.

Location / work model

• Full remote