> Markdown version of [/jobs/ext/3972739-genai-devops-engineer-ai-engineer](https://www.wearedevelopers.com/jobs/ext/3972739-genai-devops-engineer-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # GenAI DevOps Engineer /AI Engineer - **Company:** Varite Inc - **Location:** Santa Monica, CA, United States - **Experience:** Expert - **Salary:** $166,400.0 - $208,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Cloud Computing Security, Continuous Delivery, Continuous Integration, DevOps, Github, Identity and Access Management, Python (Programming Language), TypeScript, Data Logging, Scripting, Claude Code, AgentCore, Agentic-AI, Amazon Bedrock, Claude, Virtual Agents, Agent SDK, Docker - **Published:** October 27, 2026 - **Apply:** https://www.careerbuilder.com/job-details/genai-devops-engineer-ai-engineer-los-angeles-ca--7b8f05af-4052-427c-a276-7a5950942c24 ## About the Role DevOps, Artificial Intelligence (AI), Artificial Intelligence (AI), DevOps, Continuous Deployment/Delivery, Continuous Integration, Amazon Web Services (AWS), GitHub, Artificial Intelligence (AI) Agents, Python Programming/Scripting Language, AI Agent Engineer (Claude SDK / AWS AgentCore / GitHub Actions) Experience: 6-10 years overall, including hands-on GenAI or agentic AI development. ## Description GenAI DevOps Engineer /AI Engineer who has experience building AI agents and deploying them into production on AWS. The role combines AI agent development (60%) and DevOps/CI/CD engineering (40%), 1. Design, develop, and implement AI agents using the Claude Agent SDK (formerly Claude Code SDK). 2. Build agent workflows using Python or TypeScript, including tool calling, MCP integrations, and API orchestration. 3. Deploy and manage AI agents on Amazon Bedrock AgentCore Runtime. 4. Develop automated CI/CD pipelines using GitHub Actions for agent builds, testing, and deployments. 5. Configure AWS IAM, ECR, Docker/container packaging where applicable, and cloud security. 6. Monitor, troubleshoot, and optimize production AI agent performance. 7. Implement secure authentication, logging, monitoring, and observability.