Kastle

Kastle

AI Deployments Lead

San Francisco

Sponsorship not specifiedDetected 2 days ago
Data EngineeringLLMsAgentic AIAccount ManagementCustomer Success

About the role

  • Our usage has grown 40x in the last 6 months and our Agent Product and Success team is a high impact group that shapes how customers use and gain value from our product.

Responsibilities

  • Find and drive upsell and expansion opportunities across every account, growing net ARR
  • Own our ticketing system and make it world-class
  • Overtime you will step in as the leader and own every deployment across the company.

Requirements

  • 4+ years in a high-rigor customer-facing role: technical account management, solutions engineering, implementation, consulting, or banking
  • Hands-on LLM experience.
  • Comfort reading API docs and understanding concepts like webhooks, integrations, data mapping, and data pipelines
  • Experience working with executive stakeholders across operations, compliance, product, engineering, and commercial teams
  • Comfort saying "no" or "not yet" when the ask does not match product, technical, or compliance reality

Compensation

  • Competitive Compensation + Meaningful Equity at a high growth company

Benefits

  • Own our most strategic deployments end to end - timeline, health, adoption, and the customer relationship

Company info

  • Kastle is building the AI employee for consumer lending.
  • We work with some of America's largest banks and mortgage lenders, helping them scale their contact center and compliance operations using autonomous AI Agents that process payments, originate loans, and help customers get a world class experience during the biggest financial decisions of their lives.
  • We've processed over $1 billion in transactions and are backed by Y Combinator and Commerce Ventures.
  • If you are interested in building AI that is applied in the real world where precision, reliability, and performance matter and redefine access to credit across the world, apply below.

This listing is sourced directly from Kastle's careers page and normalized into a canonical job model.