AI Data Engineer, Ring/Blink Customer Service Engineering and Insights

We're hiring an AI Data Engineer to build and scale AI-powered analytics tools for Ring & Blink Customer Service. You'll turn AI prototypes into production systems that business users rely on daily — conversational analytics agents, AI teammates, self-service data tools, and intelligent automation.

Build and deploy conversational analytics agents that let users query CS data in natural language

Productionize AI teammates and agents for specific use cases — transcript analysis, metrics Q&A, contact summarization, pipeline monitoring — using internal platforms and cloud-hosted agent frameworks

Wire together the full stack: data sources (Redshift, S3) → AI layer (LLMs, agents, semantic logic) → user interface

Own the end-to-end delivery: from prototype handoff through production deployment, user onboarding, and iteration

Build validation mechanisms — does the AI answer match the source of truth?

Define and maintain the semantic layer: metric definitions, business logic, allowed data scope

Design guardrails: what data can AI access, what questions are in scope, how to handle uncertainty

Own the permission architecture for AI tools (user groups, access policies, cross-account controls)

Implement confidence scoring, audit trails, and feedback loops

Monitor AI tool performance, accuracy, and usage

Respond to user feedback and iterate — fix what's broken, improve what's clunky

Build automated validation and alerting for AI outputs

Scale successful patterns to new use cases and new user groups

Document what you build so others can extend it

When you build something that works, package it: shared agents, reusable skills, prompt templates, standard workflows

Contribute to the team's AI development practices — not by mandating, but by building things others want to copy

Keep the team current on what's working and what's not in the AI tooling landscape