Agentic WorkSpaces AI Solutions Architect - Hands-On

Amazon; Seattle (WA); On-site; USD 154,000 - 208,000; Full time

Job summary

Amazon is seeking a hands-on Applied AI Solutions Architect to accelerate production-ready Agentic WorkSpaces deployments. You will embed with customers to guide data readiness, model selection via Bedrock, prompt and tool configuration, and secure data access patterns across cloud data systems.

The role requires deep technical execution, including code writing, integrations, and co-building with customer teams, with travel up to 25-40% for on-site engagements.

Qualifications

• 7+ years of technology domain experience (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics).
• 3+ years of design, implementation, or consulting in applications and infrastructures.
• Experience with Python, Ruby, Node.js, C#, or C++. Hands-on with agentic AI patterns (multi-agent orchestration, tool use, chain-of-thought reasoning).
• Familiarity with MCP and/or A2A protocols; experience with Amazon Bedrock or equivalent foundation models.
• Understanding of Virtual Desktop Infrastructure (VDI) and Desktop-as-a-Service (DaaS).

Responsibilities

• Lead technical discovery sessions with customer teams and translate findings into actionable plans for production deployment.
• Design and configure agentic AI solutions within Agentic WorkSpaces including AI agents, prompts, and tool integrations.
• Build serverless integrations using AWS Lambda, API Gateway, Step Functions and scripting (Python, Node.js).
• Architect secure access patterns to cloud data systems (DynamoDB, RDS, S3) to power AI agent tools and RAG.

Skills

Software development

Cloud computing

Systems engineering

Infrastructure

Security

Networking

Data & analytics

Tools

AWS Lambda

API Gateway

Step Functions

Python

Node.js

Job description

Amazon is seeking a hands-on Applied AI Solutions Architect to accelerate production-ready Agentic WorkSpaces deployments. You will embed with customers to guide data readiness, model selection via Bedrock, prompt and tool configuration, and secure data access patterns across cloud data systems.

The role requires deep technical execution, including code writing, integrations, and co-building with customer teams, with travel up to 25-40% for on-site engagements.