AI Agent Engineer (Claude SDK / AWS AgentCore / GitHub Actions)

Position

Senior GenAI DevOps Engineer /AI Engineer

Remote with travel

Contract position

Notes

• The key is time zone coverage and so we can expand our search across the West Coast - remote/hybrid but necessarily located in the western time zones. You must be comfortable traveling to the client site for workshops/meetings.
• Senior GenAI DevOps Engineer /AI Engineer who has 10-15+ years of overall experience and 6-8 years building AI agents and deploying them into production on AWS.
• The role combines AI agent development (60%) and DevOps/CI/CD engineering (40%). This is a suggested split based on the responsibilities you've described.

Job Description AI Agent Engineer (Claude SDK / AWS AgentCore / GitHub Actions)

Experience: 10-15+ years overall, including hands-on GenAI or agentic AI development.

Key responsibilities:

• Design, develop, and implement AI agents using the Claude Agent SDK (formerly Claude Code SDK).
• Build agent workflows using Python or TypeScript, including tool calling, MCP integrations, and API orchestration.
• Deploy and manage AI agents on Amazon Bedrock AgentCore Runtime.
• Develop automated CI/CD pipelines using GitHub Actions for agent builds, testing, and deployments.
• Configure AWS IAM, ECR, Docker/container packaging where applicable, and cloud security.
• Monitor, troubleshoot, and optimize production AI agent performance.
• Implement secure authentication, logging, monitoring, and observability.

Skill

• Claude Agent SDK
• Amazon Bedrock AgentCore Runtime
• GitHub Actions
• Python or TypeScript
• AWS Bedrock
• Docker, AWS ECR, IAM
• MCP and tool calling
• Terraform / AWS CDK
• CloudWatch, security, monitoring

Priority

• Must-have
• Must-have
• Must-have
• Must-have
• Must-have
• Strongly preferred
• Strongly preferred
• Good-to-have
• Good-to-have

description

• Hands-on agent development, not just Claude prompting
• Actual agent deployment and runtime configuration
• Building CI/CD pipelines independently
• Strong coding and API integration
• Model integration and AWS ecosystem knowledge
• Packaging, deployment, permissions
• Building agents that execute external tools
• Infrastructure as Code
• Production support