Senior Product Manager – AI Platform

Role Overview

• 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.

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 PM role
• 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.

Tech Stack

• ETL
• Kafka

Benefits

• Insurance coverage (medical, dental, vision, life, and disability)
• Flexible paid time off
• Paid holidays
• 401(k) plan with company match
• Remote work