Senior​/Lead Product Manager - Core AI Platform

Senior/Lead Product Manager - Core AI Platform

Banking is being reimagined—and customers expect every interaction to be easy, personal, and instant
.

We are building a universal banking assistant that millions of U.S. consumers can use to transact across all financial institutions and, over time,
autonomously drive their financial goals
. Powered by our proprietary BankGPT platform
, this assistant is positioned to displace age-old legacy systems within financial institutions and own the end-to-end CX stack
, unlocking a $200B opportunity and potentially replacing multiple publicly traded companies
.

Ultimately,
our mission is to drive financial well-being for millions of consumers.

With over two-thirds of Americans living paycheck to paycheck, 50% holding less than $500 in savings, and only 17% financially literate,
we aim to
put financial well-being on autopilot to help solve this problem.

As a Senior/Lead Product Manager – Core AI Platform, you will own the vision, roadmap, and execution for the Core Agentic AI Platform that powers all interface.ai products.

This is a foundational, deeply technical role. You will define the platform primitives that enable:

• Core agentic behavior (planning, goal routing, memory, context switching, tool use)
• Safe and compliant AI operation in regulated environments (PII controls, auditability, policy enforcement)
• Scalable, low‑latency inference and multi‑model orchestration across voice and chat experiences
• Expansion beyond a single vertical by building reusable, configurable platform capabilities

You will partner tightly with Core AI Engineering, Research, Product Engineering, Design, and GTM/Delivery teams to turn platform capabilities into measurable product outcomes.

• Set platform strategy for the agent runtime layer: multi‑agent orchestration, memory/context, tool routing, and policy‑aligned behavior.
• Prioritize platform investments that scale across product lines and enable future vertical expansion.
• Define clear platform contracts so product teams can reliably build on the platform.

• Drive the roadmap for model selection, evaluation, fine‑tuning enablement, and benchmarking.
• Partner with engineering to define workflows and requirements for fine‑tuning pipelines, dataset strategy, and safe experimentation.
• Establish decision frameworks for when to prompt‑tune vs fine‑tune vs switch models, balancing quality, latency, and cost.

• Define product requirements for high‑throughput, low‑latency inference and runtime efficiency (caching, batching, quantization strategy, token efficiency).Establish reliability patterns: multi‑region deployments, fallbacks, graceful degradation, and safe rollouts (flags/canaries/rollback).
• Build cost/latency governance: budgets, monitoring, and optimization priorities across high‑scale deployments.

• Own platform‑level requirements for automated PII detection/masking, prompt/response safety policies, and data handling controls.
• Drive secure‑by‑default platform capabilities: tenant isolation, encryption expectations, retention controls, audit logs, and access control requirements.
• Ensure the platform can support compliance needs (e.g., SOC2/GDPR readiness) through measurable controls and operational rigor.

• Establish the eval strategy and roadmap: offline golden sets, regression testing, online quality metrics, and automated safety checks.
• Define how teams measure factual accuracy, hallucination risk, task success, latency, and cost efficiency—then make it actionable via tooling and dashboards.
• Create feedback loops from production to improve prompts/models/policies continuously.

• Drive platform requirements for real‑time conversational intelligence: ASR/TTS integration patterns, latency budgets, and quality metrics (WER, interruption handling, turn‑taking).
• Prioritize multimodal platform primitives that improve naturalness, responsiveness, and…