Sr. Applied AI Solutions Architect Public Sector, Amazon Connect
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Job Location:
Arlington, TX - USA
Monthly Salary:
Not Disclosed
Posted on:
Yesterday
Vacancies:
1 Vacancy
Job Summary
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 Connects 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 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 customers 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 ) 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.
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 customers 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 ) 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.
A day in the life
Pair-programming with customer developers to build and test AI agent configurations
- Designing prompt strategies and evaluating model performance across different foundation models
- Configuring MCP servers to expose customer APIs databases and tools in a standardized format for agent consumption
- Designing A2A workflows where Amazon Connect agents hand off to or collaborate with specialized agents across the customers enterprise
- Configuring knowledge bases and data connectors for RAG-powered agent responses
- Conducting architecture reviews and providing prescriptive guidance for production readiness
- Documenting implementation patterns and contributing to the teams knowledge base
Participating in weekly syncs with Connect service teams to share customer feedback and product insights
About the team
The Applied AI Solutions Architecture team is part of the AWS Specialist and Partner Organization (ASP). We are the technical bridge between Amazon Connect customers and the service teams building the next generation of AI-powered contact center capabilities. Our team operates at the forefront of agentic AI adoption helping customers become production-ready with Amazon Connects Unlimited AI features.
- 3 years of design implementation or consulting in applications and infrastructures experience
- 7 years of specific technology domain areas (e.g. software development cloud computing systems engineering infrastructure security networking data & analytics) experience
- 7 years of IT development or implementation/consulting in the software or Internet industries experience
- Experience with Amazon Connect or other enterprise contact center platforms (Genesys Avaya Cisco NICE Five9 etc.)
- Hands-on experience with Amazon Bedrock including model invocation agent creation knowledge base configuration and guardrails
- 5 years of infrastructure architecture database architecture and networking experience
- Experience with agentic AI patterns multi-agent orchestration tool use function calling chain-of-thought reasoning and autonomous agent workflows .
- Hands-on experience building and deploying MCP servers exposing enterprise tools and APIs via Model Context Protocol for dynamic agent tool discovery and invocation
- Experience designing A2A (Agent-to-Agent) architectures enabling specialized agents to collaborate across domains (e.g. billing logistics IT) through standardized agent communication protocols
- Proficiency with agentic IDEs such as Kiro Cursor or similar AI-assisted development environments including experience with agent hooks agent steering MCP server configuration and spec-driven development
- AWS certifications (Solutions Architect Professional AI Practitioner Machine Learning Specialty)
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status disability or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.
The Applied AI Solutions Architecture team within AWS is seeking a hands-on customer-obsessed Solutions Architect to accelerate customer adoption of Amazon Connects 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 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 customers 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 ) 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.
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 customers 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 ) 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.
A day in the life
Pair-programming with customer developers to build and test AI agent configurations
- Designing prompt strategies and evaluating model performance across different foundation models
- Configuring MCP servers to expose customer APIs databases and tools in a standardized format for agent consumption
- Designing A2A workflows where Amazon Connect agents hand off to or collaborate with specialized agents across the customers enterprise
- Configuring knowledge bases and data connectors for RAG-powered agent responses
- Conducting architecture reviews and providing prescriptive guidance for production readiness
- Documenting implementation patterns and contributing to the teams knowledge base
Participating in weekly syncs with Connect service teams to share customer feedback and product insights
About the team
The Applied AI Solutions Architecture team is part of the AWS Specialist and Partner Organization (ASP). We are the technical bridge between Amazon Connect customers and the service teams building the next generation of AI-powered contact center capabilities. Our team operates at the forefront of agentic AI adoption helping customers become production-ready with Amazon Connects Unlimited AI features.
- 3 years of design implementation or consulting in applications and infrastructures experience
- 7 years of specific technology domain areas (e.g. software development cloud computing systems engineering infrastructure security networking data & analytics) experience
- 7 years of IT development or implementation/consulting in the software or Internet industries experience
- Experience with Amazon Connect or other enterprise contact center platforms (Genesys Avaya Cisco NICE Five9 etc.)
- Hands-on experience with Amazon Bedrock including model invocation agent creation knowledge base configuration and guardrails
- 5 years of infrastructure architecture database architecture and networking experience
- Experience with agentic AI patterns multi-agent orchestration tool use function calling chain-of-thought reasoning and autonomous agent workflows .
- Hands-on experience building and deploying MCP servers exposing enterprise tools and APIs via Model Context Protocol for dynamic agent tool discovery and invocation
- Experience designing A2A (Agent-to-Agent) architectures enabling specialized agents to collaborate across domains (e.g. billing logistics IT) through standardized agent communication protocols
- Proficiency with agentic IDEs such as Kiro Cursor or similar AI-assisted development environments including experience with agent hooks agent steering MCP server configuration and spec-driven development
- AWS certifications (Solutions Architect Professional AI Practitioner Machine Learning Specialty)
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status disability or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience qualifications and location. Amazon also offers comprehensive benefits including health insurance (medical dental vision prescription Basic Life & AD&D insurance and option for Supplemental life plans EAP Mental Health Support Medical Advice Line Flexible Spending Accounts Adoption and Surrogacy Reimbursement coverage) 401(k) matching paid time off and parental leave. Learn more about our benefits at NY New York - 169000.00 - 228600.00 USD annually
USA VA Arlington - 153600.00 - 207800.00 USD annually
USA WA Seattle - 153600.00 - 207800.00 USD annually
Required Experience:
Senior IC
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