Sr. Applied AI Solutions Architect - Public Sector, Amazon Connect

Amazon Amazon · Big Tech · Arlington, VA · Solutions Architect

Sr. Applied AI Solutions Architect focused on accelerating customer adoption of Amazon Connect's AI capabilities. The role involves guiding customers in model selection (via Amazon Bedrock), prompt configuration for AI agents, and architecting tool integrations (APIs, Lambda, etc.) for agentic AI systems. A key aspect is ensuring customer data readiness for AI agents and RAG. The role is hands-on, requiring coding, building integrations, and configuring agents, working at the intersection of contact center operations and applied AI.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Skills

Required

  • Amazon Connect AI capabilities
  • foundation models (Amazon Bedrock)
  • prompt engineering
  • agentic AI systems
  • tool integration (APIs, Lambda functions, data connectors, knowledge bases)
  • customer data readiness assessment and structuring
  • data pipelines
  • RAG
  • AWS Lambda
  • API Gateway
  • Step Functions
  • Python
  • Node.js
  • Amazon DynamoDB
  • Amazon RDS
  • Amazon S3
  • Amazon OpenSearch
  • Amazon Kendra/Knowledge Bases for Bedrock

Nice to have

  • contact center operations
  • MCP (Model Context Protocol)
  • A2A (Agent-to-Agent) communication patterns
  • multi-agent orchestration

What the JD emphasized

  • hands-on
  • customer-obsessed
  • deeply technical
  • write code
  • build integrations
  • configure agents
  • pair-program

Other signals

  • customer-facing AI solutions
  • agentic AI
  • foundation models
  • prompt engineering
  • tool integration
  • data readiness for AI
  • RAG
  • multi-agent orchestration