招聘
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 #One Team.
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.
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Key job responsibilities
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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.
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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.
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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.
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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.
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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).
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Cloud Data Access: Architect secure access patterns to cloud-based data systems to power AI agent tool use and retrieval-augmented generation (RAG).
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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.
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Pre-Production Validation: Guide customers through testing, evaluation, and validation of AI agent performance against defined success criteria before production deployment.
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Field Enablement: Share learnings, delivering technical deep-dives, and mentoring other SAs on agentic AI implementation patterns.
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A day in the life
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Conducting data readiness assessments, identifying gaps in knowledge base coverage, and recommending data preparation steps before AI agent configuration
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Designing prompt strategies and evaluating model performance across different foundation models
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Building Lambda functions and API integrations that serve as tools for AI agents
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Configuring MCP servers to expose customer APIs, databases, and tools in a standardized format for agent consumption
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Designing A2A workflows where Amazon Connect agents hand off to or collaborate with specialized agents across the customer's enterprise
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Configuring knowledge bases and data connectors for RAG-powered agent responses
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Running evaluation frameworks to measure AI agent accuracy, latency, and customer satisfaction
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Conducting architecture reviews and providing prescriptive guidance for production readiness
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Documenting implementation patterns and contributing to the team's knowledge base
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 Connect's Unlimited AI features.
Basic Qualifications
- Experience within specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics).
- Experience in design, implementation, or consulting in applications and infrastructures
- Experience communicating across technical and non-technical audiences, including executive level stakeholders or clients
Preferred Qualifications
- Experience working with and presenting to C-level executives, IT, and lines of businesses across organizations or equivalent
- AWS certification, such as, AWS Solutions Architect, or a similar cloud certification
- Knowledge of data structures, data modeling, and database schema
- Experience architecting, migrating, transforming or modernizing customer requirements to the cloud
- 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
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.
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 https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
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关于Amazon

Amazon
PublicAmazon.com, Inc. is an American multinational technology company engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence.
10,001+
员工数
Seattle
总部位置
$1.5T
企业估值
评价
2.9
10条评价
工作生活平衡
2.8
薪酬
3.7
企业文化
2.5
职业发展
2.3
管理层
2.1
35%
推荐给朋友
优点
Good pay and compensation
Strong benefits package
Flexible scheduling options
缺点
Poor management and leadership
Limited growth and promotion opportunities
High stress and demanding work environment
薪 资范围
4个数据点
L2
L3
L4
L5
L6
L2 · Data Analyst L2
0份报告
$108,330
年薪总额
基本工资
$43,332
股票
$54,165
奖金
$10,833
$75,831
$140,829
面试经验
10次面试
难度
3.7
/ 5
时长
21-35周
录用率
20%
体验
正面 10%
中性 10%
负面 80%
面试流程
1
Application Review
2
Recruiter Screen
3
Online Assessment
4
Technical Phone Screen
5
Onsite/Virtual Loop
6
Team Matching
7
Offer
常见问题
Coding/Algorithm
System Design
Behavioral/STAR
Leadership Principles
Technical Knowledge
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