GenAI DevOps Engineer /AI Engineer

TheStaffed•Los Angeles, CA

About The Position

Our client is seeking a GenAI DevOps Engineer / AI Engineer to design, develop, and deploy intelligent AI agents using Claude Agent SDK and Amazon Bedrock. The role involves building production-grade agent workflows and implementing CI/CD automation in a cloud-native environment. The engineer will configure AWS infrastructure, support secure and observable agent systems, and collaborate with cross-functional teams to integrate AI agents into broader application architectures.

Requirements

  • 6–10 years of experience in DevOps, cloud engineering, or AI/ML infrastructure.
  • Proficiency in Python and/or TypeScript for agent development and automation.
  • Strong hands-on experience with AWS services, including Bedrock, ECR, IAM, CloudWatch, and VPC configuration.
  • Expertise with CI/CD tools, particularly GitHub Actions, and infrastructure-as-code tools such as Terraform or AWS CDK.
  • Solid understanding of containerization, Docker, and container orchestration.
  • Experience with AI/ML concepts, including prompt engineering, tool calling, model calling patterns, and agent frameworks.
  • Knowledge of security best practices, authentication mechanisms, and cloud infrastructure hardening.
  • Excellent troubleshooting and problem-solving skills with a focus on production reliability and monitoring.

Responsibilities

  • Design, develop, and implement AI agents using the Claude Agent SDK and Amazon Bedrock AgentCore Runtime.
  • Build agent workflows in Python or TypeScript, including tool calling, MCP integrations, and API orchestration.
  • Develop and maintain automated CI/CD pipelines using GitHub Actions for agent builds, testing, and deployments.
  • Configure and manage AWS infrastructure, including IAM policies, ECR registries, Docker containerization, and cloud security practices.
  • Monitor, troubleshoot, and optimize production AI agent performance using CloudWatch and observability tools.
  • Implement secure authentication, logging, and monitoring for agent systems to support compliance and visibility.
  • Collaborate with cross-functional teams to integrate AI agents into broader application architectures.
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