Note: The job is a remote job and is open to candidates in USA. MeridianLink is seeking a strategic and technically fluent Senior Product Manager to own the AI platform layer that underpins their vertical SaaS offering for the lending industry. The role involves managing foundational capabilities that enable both internal engineering teams and external integrators to build AI-powered experiences, while ensuring alignment with company-wide product vision and revenue goals.
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
Skills
- 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; Computer science background (education or other demonstrated proficiency) required
- Experience in vertical SaaS required
- MBA preferred
- Consumer or mortgage lending software experience is a plus
Benefits
- REMOTE work model
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