Sr. Applied AI Solutions Architect, Amazon Connect

Application deadline: Apr 24, 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.

Are you a customer-obsessed builder with a passion for helping customers achieve their full potential? Do you have the technical background, customer experience, and skills necessary to help accelerate customer adoption of Amazon Connect's AI capabilities? Do you love building new strategic and data-driven businesses? Join the Applied AI Solutions team as an Amazon Connect Specialist Solutions Architect!

The Applied AI Solutions Architecture team is seeking a hands-on, customer-obsessed Solutions Architect to accelerate customer adoption of Amazon Connect's AI capabilities. Applied AI Solutions is part of the AWS Specialist & Partner (ASP) org, which works backwards from our customer’s most complex and business critical problems to build and execute go-to-market plans that turn AWS ideas into multi-billion-dollar businesses. We pride ourselves on thinking big, delivering exceptional results for our customers, and working across AWS as #OneTeam.

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 deployments. We stay closely connected to our customers and bring valuable data and insights to our product teams, strengthening the product roadmap. Our team is at its best when a customer is thinking big and needs specialized experience to innovate for their business.

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.
- 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 to power AI agent tool use and retrieval-augmented generation (RAG).
- Agentic IDE Proficiency: Leverage agentic development environments such as Kiro (and similar AI-assisted IDEs) to accelerate development workflows, including spec-driven development, agent hooks, MCP server configuration, and AI-assisted code generation.
- Pre-Production Validation: Guide customers through testing, evaluation, and validation of AI agent performance against defined success criteria before production deployment.
- Field Enablement: Share learnings, delivering technical deep-dives, and mentoring other SAs on agentic AI implementation patterns.

A day in the life
- Conducting data readiness assessments, identifying gaps in knowledge base coverage, and recommending data preparation steps before AI agent configuration
- Designing prompt strategies and evaluating model performance across different foundation models
- Building Lambda functions and API integrations that serve as tools for AI agents
- Configuring MCP servers to expose customer APIs, databases, an