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Ring & Blink Customer Service Technology is looking for a Solutions Architect to design and deliver AI-powered customer service systems at scale. This role sits at the intersection of Amazon Connect, Salesforce, and large language model (LLM) services, architecting the intelligent layer that powers voice AI, chat automation, and self-service experiences for millions of Ring and Blink customers.
You will own the end-to-end technical architecture for our contact center AI stack: designing LLM integration patterns with Amazon Bedrock, building AI agent orchestration across voice and chat channels, and driving the convergence of our Salesforce CRM and Amazon Connect platforms into a unified, AI-native system. This is not a traditional infrastructure role

You are the person who translates business problems into AI-powered solutions, validates them with data, and ships them into production.
We are a team that builds fast, measures everything, and treats AI as a core capability rather than an experiment. You will work directly with CS Operations leadership, product managers, and engineering to design systems that increase automation, reduce cost per contact, and improve the customer experience.
Key job responsibilities
- Design and implement AI-powered contact flow architectures using Amazon Connect, Amazon Bedrock, and Salesforce AI services (AgentForce, Einstein)
- Own the technical architecture for voice AI systems: IVR-to-LLM handoff patterns, intent classification, real-time transcription pipelines, and containment optimization
- Architect RAG (Retrieval Augmented Generation) implementations that connect knowledge bases to AI agents for accurate, context-aware customer interactions
- Lead the Salesforce/Connect convergence architecture, ensuring AI capabilities are natively integrated rather than bolted on
- Design prompt engineering frameworks and evaluation systems that measure AI output quality, hallucination rates, and customer satisfaction impact
- Build AI agent orchestration patterns across channels (voice, chat, self-service portals) with consistent fallback and escalation logic
- Create technical design documents, integration specifications, and architecture diagrams that engineering teams can execute against
- Partner with AWS service teams on Connect AI features (Connect Assistant, Agent Assist, Q in Connect) to evaluate, pilot, and integrate new capabilities
- Develop A/B testing frameworks for AI features, measuring containment rate, resolution rate, CSAT, and cost per contact
- Collaborate with CS Operations, Workforce Management, and Quality Assurance to ensure AI solutions meet operational requirements and scale targets
A day in the life
You start your day joining a working session with the engineering team to finalize the integration design for a new Bedrock-powered summarization service that will give agents real-time context from prior customer contacts. You are mapping the data flow, defining the API contracts, and mapping interdependencies

After lunch, you are on a call with the AWS Connect team reviewing a new AI feature in preview. You assess whether it fits your architecture, what the integration lift looks like, and whether it solves a real operational pain point. You document your recommendation and share it with the product manager.
This role requires someone who is equally comfortable in a technical design review and a business strategy discussion with operations stakeholders

You are the connective tissue between what AI can do technically and what CS Operations needs it to do in practice.
About the team
Ring & Blink CS Technology Enablement owns the systems that power customer service for Ring, Blink, and Key by Amazon. We operate at the intersection of AI, contact center technology, and operational excellence.
We are a team of builders who ship fast and iterate weekly. AI is not a side project here

It is the core of our strategy: automate what we can, empower associates with AI for everything else, and measure the impact of both. You will join a team that has already deployed production voice AI, built LLM-powered agent assist tools, and is actively expanding AI capabilities across every customer touchpoint.