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# Senior Generative AI Developer

- **Company:** Capgemini
- **Location:** St. Louis, MO, United States
- **Experience:** Expert
- **Salary:** $67,309.0 - $105,165.0
- **Contract:** Temporary contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Cloud Computing Security, Identity and Access Management, Python (Programming Language), Software Deployment, TypeScript, Multi-Agent Systems, Prompt Engineering, Generative AI, AWS Lambda, Backend, Production Code, Api Gateway
- **Published:** July 24, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=46bb8ad4d933a7af

## About the Role

* AWS Bedrock Ecosystem: Mastery of foundational models, prompt engineering, and Amazon Bedrock Agents.
 * AWS Bedrock AgentCore: Production experience with AgentCore Runtime, Gateway, Memory, Identity, and Observability.
 * AWS Kiro: Mastery of Kiro IDE, Agent Toolkit for AWS, and building specialized Kiro Powers.
 * Multi-Agent Orchestration: Expertise in building A2A communication, collaborative workflows, and Frameworks like Strands, CrewAI, or LangGraph.
 * Integration Protocols: Deep understanding of Model Context Protocol (MCP) server architectures.
 * Backend Development: Strong proficiency in Python or TypeScript, AWS Lambda, and API Gateway.

## Description

Production Deployment
aws cloud
python development
AWS Bedrock
Generative AI
solution architecture
cloud security

Senior Generative AI Developer (Agentic Systems) to lead the design, development, and deployment of production-grade AI agents using the latest AWS agentic infrastructure. In this role, you will leverage AWS Bedrock AgentCore and AWS Kiro to build complex, secure, and highly scalable multi-agent systems featuring advanced Agent-to-Agent (A2A) communication and orchestration protocols.

Core Responsibilities:

 * Design Agent Systems: Architect scalable multi-agent systems using Amazon Bedrock AgentCore Runtime and Gateway.
 * Implement Collaboration: Build robust Agent-to-Agent (A2A) communication patterns enabling supervisor/sub-agent handoffs.
 * Optimize IDE Workflows: Use AWS Kiro IDE and Kiro Powers to turn natural language prompts into production code.
 * Integrate Protocols: Connect agents to external enterprise resources, legacy APIs, and tools via the Model Context Protocol (MCP).
 * Manage Agent Memory: Build stateful agent sessions using AgentCore conversational memory modules.
 * Enforce Cloud Security: Establish deterministic IAM authorization patterns to protect secure microVM agent environments.


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