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.
The team has a clear AI vision, active prototypes, and an engineering culture where everyone already uses AI in their daily work. Your job is to take ideas to production, keep them reliable, expand to new use cases, and own what you ship. You'll build fast, ship often, and iterate based on real user feedback.
The team uses a mix of AI tools — some running locally for individual productivity, others deployed in the cloud for scalability and broader user access. You'll work across both: building AI agents and tools that range from developer-facing automation to user-facing analytics products.
You'll work alongside data engineers, platform engineers, and BI engineers who own the underlying data infrastructure and dashboards. You own the AI-powered layer on top — the part that makes data accessible, answers trustworthy, and users self-sufficient.
Key job responsibilities
Build AI-Powered Data Products (45%)
Business users shouldn't need to file a ticket to get answers. You build the tools that make them independent.
* 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
Ensure Correctness & Governance (25%)
AI tools that give wrong answers are worse than no tools at all. You make them trustworthy.
* 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
Maintain, Monitor & Scale (20%)
Shipped is not done. You keep AI products healthy and improving.
* 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
Standardize AI Patterns for the Team (10%)
The team already uses AI individually. You make the best patterns reusable.
* 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
