Senior Applied AI Solutions Architect — Amazon Connect
Amazon, San Francisco, CA, United States
Senior Applied AI Solutions Architect – Amazon Connect
Job ID: 10390088 | Amazon Web Services, Inc.
Application deadline: Apr 28, 2026
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, balancing latency, accuracy, cost, and compliance.
Prompt Configuration – designing, testing, and optimizing AI prompts and system instructions for Amazon Connect AI agents.
Tool Configuration – architecting and building tool integrations (APIs, Lambda functions, data connectors, knowledge bases) that agentic AI systems use to take actions on behalf of customers and agents.
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 knowledge bases, CRMs, and backend systems are AI‑ready before agents go live.
Key Job Responsibilities
Lead technical discovery sessions with customer teams to understand business requirements, existing contact center architecture, and AI readiness. Translate findings into actionable implementation plans.
Conduct data readiness assessments, recommend remediation strategies, and help customers build the data foundation required for effective AI agent tool use and retrieval‑augmented generation.
Design and configure agentic AI solutions within Amazon Connect, including AI agent creation, prompt engineering, model selection, guardrail configuration, and tool/action integration.
Design and deploy Model Context Protocol (MCP) servers that expose customer tools, data sources, and APIs in a standardized format for AI agent discovery and invocation.
Architect Agent‑to‑Agent (A2A) communication patterns that allow Amazon Connect AI agents to collaborate with specialized agents across the enterprise.
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.
Architect secure access patterns to cloud‑based data systems (Amazon DynamoDB, Amazon RDS, Amazon S3, Amazon OpenSearch, Amazon Kendra) to power AI agent tool use and retrieval‑augmented generation.
Guide customers through testing, evaluation, and validation of AI agent performance against defined success criteria before production deployment.
Create reusable artifacts such as reference architectures, implementation guides, sample code, prompt libraries, and data readiness checklists.
Provide feedback to Amazon Connect and Amazon Bedrock product teams based on real‑world customer implementations.
Qualifications
7+ years of experience in technology domains such as software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics.
3+ years of design, implementation, or consulting experience in applications and infrastructures.
Can travel to local sites up to 25% of the time.
Familiarity with interoperability protocols such as MCP (Model Context Protocol) and/or A2A (Agent‑to‑Agent) for multi‑agent communication.
Demonstrated experience with AI/ML concepts including large language models (LLMs), prompt engineering, retrieval‑augmented generation (RAG), and model evaluation.
Preferred Qualifications
2+ years of contact center experience, or experience with AWS services or other cloud offerings.
Experience with CI/CD pipelines build processes.
AWS certification, such as AWS Solutions Architect, or a similar cloud certification.
Bachelor's degree or above in computer science, machine learning, engineering, or related fields.
Hands‑on experience with Amazon Bedrock, including model invocation, agent creation, knowledge base configuration, and guardrails.
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.
Amazon is an equal‑opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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Job ID: 10390088 | Amazon Web Services, Inc.
Application deadline: Apr 28, 2026
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, balancing latency, accuracy, cost, and compliance.
Prompt Configuration – designing, testing, and optimizing AI prompts and system instructions for Amazon Connect AI agents.
Tool Configuration – architecting and building tool integrations (APIs, Lambda functions, data connectors, knowledge bases) that agentic AI systems use to take actions on behalf of customers and agents.
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 knowledge bases, CRMs, and backend systems are AI‑ready before agents go live.
Key Job Responsibilities
Lead technical discovery sessions with customer teams to understand business requirements, existing contact center architecture, and AI readiness. Translate findings into actionable implementation plans.
Conduct data readiness assessments, recommend remediation strategies, and help customers build the data foundation required for effective AI agent tool use and retrieval‑augmented generation.
Design and configure agentic AI solutions within Amazon Connect, including AI agent creation, prompt engineering, model selection, guardrail configuration, and tool/action integration.
Design and deploy Model Context Protocol (MCP) servers that expose customer tools, data sources, and APIs in a standardized format for AI agent discovery and invocation.
Architect Agent‑to‑Agent (A2A) communication patterns that allow Amazon Connect AI agents to collaborate with specialized agents across the enterprise.
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.
Architect secure access patterns to cloud‑based data systems (Amazon DynamoDB, Amazon RDS, Amazon S3, Amazon OpenSearch, Amazon Kendra) to power AI agent tool use and retrieval‑augmented generation.
Guide customers through testing, evaluation, and validation of AI agent performance against defined success criteria before production deployment.
Create reusable artifacts such as reference architectures, implementation guides, sample code, prompt libraries, and data readiness checklists.
Provide feedback to Amazon Connect and Amazon Bedrock product teams based on real‑world customer implementations.
Qualifications
7+ years of experience in technology domains such as software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics.
3+ years of design, implementation, or consulting experience in applications and infrastructures.
Can travel to local sites up to 25% of the time.
Familiarity with interoperability protocols such as MCP (Model Context Protocol) and/or A2A (Agent‑to‑Agent) for multi‑agent communication.
Demonstrated experience with AI/ML concepts including large language models (LLMs), prompt engineering, retrieval‑augmented generation (RAG), and model evaluation.
Preferred Qualifications
2+ years of contact center experience, or experience with AWS services or other cloud offerings.
Experience with CI/CD pipelines build processes.
AWS certification, such as AWS Solutions Architect, or a similar cloud certification.
Bachelor's degree or above in computer science, machine learning, engineering, or related fields.
Hands‑on experience with Amazon Bedrock, including model invocation, agent creation, knowledge base configuration, and guardrails.
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.
Amazon is an equal‑opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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