Senior Applied AI Solutions Architect - Amazon Connect, Applied AI SA - AIVT

About the role

Application deadline: Jun 6, 2026 This position is part of the AWS Specialist and Partner Organization (ASP). Specialists own the end-to-end go-to-market strategy for their respective technology domains, providing the business and technical expertise to help our customers succeed. Partner teams own the strategy, recruiting, development, and growth of our key technology and consulting partners. Together they provide our customers with the expertise and scale needed to build innovative solutions for their most complex challenges. 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. This role is part of the AI Velocity Team - a service-specific approach that assigns dedicated advisory and hands-on development resources directly to customers to achieve production-ready outcomes in weeks instead of months. 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 - Guiding customers through evaluating and selecting the right foundation models (via Amazon Bedrock) for their contact center use cases, balancing latency, accuracy, cost, and compliance requirements. Prompt Configuration - Designing, testing, and optimizing AI prompts and system instructions for Amazon Connect AI agents, including self-service agents, answer recommendation agents, and custom orchestrator agents. Tool Configuration - Architecting and building the tool integrations (APIs, Lambda functions, data connectors, knowledge bases) that agentic AI systems use to take actions on behalf of customers and agents - including configuring MCP (Model Context Protocol) servers for standardized tool discovery and invocation, and enabling A2A (Agent-to-Agent) communication patterns for multi-agent orchestration across enterprise systems. 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 help customers evaluate their data landscape, identify gaps, establish data pipelines, and ensure their knowledge bases, CRMs, and backend systems are AI-ready before agents go live. 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. Willingness to travel up to 25-40% for on-site customer engagements Key job responsibilities 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 (refere