The Applied AI Solutions Architecture team within AWS is seeking a hands-on, customer-obsessed Solutions Architect to accelerate customer adoption of Amazon Connect's AI capabilities. As an Applied AI Solutions Architect, you will be embedded with customers to help them prepare their Amazon Connect implementations for production by focusing on three critical pillars of agentic AI: Model Selection, Prompt Configuration, and Tool Configuration. A critical dimension of this role is Customer Data Readiness — assessing, preparing, and structuring customer data assets so that AI agents can reliably access, retrieve, and act on the right information. You will work at the intersection of contact center operations and applied AI, helping customers move from proof-of-concept to pre-production for their Amazon Connect + Unlimited AI deployments. This is a deeply technical, hands-on role — you will write code, build integrations, configure agents, and pair-program with customer engineering teams.

- Customer Engagement: Lead technical discovery sessions with customer teams to understand business requirements, existing contact center architecture, and AI readiness. Translate findings into actionable implementation plans.

- Customer Data Readiness: Conduct data readiness assessments to evaluate the quality, accessibility, structure, and governance of customer data assets (CRMs, knowledge bases, ticketing systems, order management, etc.). Identify data gaps, recommend remediation strategies, and help customers build the data foundation required for effective AI agent tool use and RAG-powered responses.

- Agentic AI Implementation: Design and configure agentic AI solutions within Amazon Connect, including AI agent creation, AI prompt engineering, model selection, guardrail configuration, and tool/action integration.

- MCP Server Configuration: Design and deploy Model Context Protocol (MCP) servers that expose customer tools, data sources, and APIs in a standardized format — enabling AI agents to dynamically discover and invoke capabilities across the customer's technology stack.

- A2A (Agent-to-Agent) Integration: Architect Agent-to-Agent communication patterns that allow Amazon Connect AI agents to collaborate with specialized agents across the enterprise (e.g., billing agents, order management agents, IT support agents), enabling multi-agent workflows that span organizational boundaries.

- Integration Development: Build serverless integrations using AWS Lambda, API Gateway, Step Functions, and scripting (Python, Node.js) to connect Amazon Connect AI agents with customer data systems (CRMs, ERPs, databases, knowledge bases).

- Cloud Data Access: Architect secure access patterns to cloud-based data systems (Amazon DynamoDB, Amazon RDS, Amazon S3, Amazon OpenSearch, Amazon Kendra/Knowledge Bases for Bedrock) to power AI agent tool use and retrieval-augmented generation (RAG).

- Pre-Production Validation: Guide customers through testing, evaluation, and validation of AI agent performance against defined success criteria before production deployment.

- Knowledge Sharing: Create reusable artifacts (reference architectures, implementation guides, sample code, prompt libraries, data readiness checklists) that scale best practices across the Connect SA community and partner ecosystem.

- Service Team Collaboration: Provide feedback to Amazon Connect and Amazon Bedrock product teams based on real-world customer implementations, contributing to product roadmap prioritization.