Senior AI Solutions Architect: Agentic AI for Connect

Amazon

Arlington (VA)

On-site

USD 154,000 - 208,000

Full time

6 days ago

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Benefits offered by this job

Health insurance

401(k) matching

paid time off

parental leave

Job summary

Amazon Web Services (AWS) seeks a hands-on Solutions Architect to accelerate adoption of Amazon Connect's AI capabilities. You will embed with customers to prepare Connect implementations for production, focusing on data readiness and tool integrations.

The role involves writing code, building integrations, configuring agents, and pair‑programming with customer engineering teams, with travel up to 25‑40% for on‑site engagements in the Arlington area.

Qualifications

6+ years of design, implementation, or consulting in applications and infrastructures experience.
Experience with Amazon Connect or other enterprise contact center platforms.
Hands-on experience with Amazon Bedrock, including model invocation, agent creation, knowledge base configuration, and guardrails.

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 (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.