DevOps Engineer
Eoja Open · verified Oct 24, 2026 At a glance
Mid-level DevOps & Release role at Eoja. Carlsbad · full-time · no salary stated · we estimate $120,000–$167,500 across the United States.
Eoja has not published a salary for this role. The figure below is our estimate, not theirs.
devopsroles summary, based on the employer's posting.
What you'll do
- Build GitHub-based CI/CD workflows for deploying autonomous AI agents and their supporting software
- Automate cloud infrastructure that supports AI agent launches, growth and ongoing operations
- Track agent performance through monitoring, logs and alerts, then respond to reliability issues
- Work with AI engineers and architects to improve model training, deployment and update workflows
- Protect AI pipelines with secrets management and access controls, and connect QA tools to testing workflows
What you bring
- At least four years in DevOps, MLOps or infrastructure engineering
- Hands-on background automating infrastructure for AI agent workloads
- Experience with Docker, Kubernetes, Terraform, GitHub Actions and hybrid cloud deployments
- Production monitoring experience with Dynatrace, plus familiarity with agent frameworks such as LangChain or LangGraph
Who this fits
This mid-level, full-time role suits someone who can support AI agent systems across cloud and hybrid infrastructure. You’ll work onsite in Carlsbad and collaborate with AI engineering, infrastructure and QA teams.
From the employer
Location
Carlsbad, CA United States (Onsite)
Type
Full-time
Role Summary
The DevOps Engineer will own the deployment, scaling, and monitoring of GenAI systems across cloud-native and hybrid platforms. You'll collaborate with AI Engineers and infrastructure teams to ensure reliable and secure AI operations.
Key Responsibilities
- Design and manage CI/CD pipelines using GitHub
- Develop and maintain robust CI/CD pipelines tailored specifically for deploying autonomous agentic AI systems and related software components.
- Build and automate cloud infrastructure optimized for deploying, scaling, and managing autonomous AI agents.
- Implement comprehensive monitoring, logging, and alerting for agentic AI processes, ensuring reliability, performance, and continuous improvement.
- Collaborate closely with Agentic AI engineers and architects to streamline workflows for training, deploying, and updating agentic AI models.
- Manage infrastructure security, access control, and compliance related to sensitive AI data and automated agent operations.
- Containerize and deploy LLM apps on hybrid infrastructure (e.g., public cloud, on-prem)
- Configure observability platforms such as Dynatrace for observability and incident response
- Automate integration and event-driven workflows using n8n
- Secure AI pipelines, manage secrets, and enable role-based access controls
- Collaborate with QA for integration with test automation tools such as Functionize, Mabl, and Port
Requirements
- 4+ years in DevOps, MLOps, or infrastructure engineering
- Extensive experience with automation and infrastructure management supporting AI agent workloads.
- Expertise with containerization (Docker, Kubernetes), IaC (Terraform), and orchestration tools for agentic AI platforms.
- Familiarity with platforms and frameworks commonly used for autonomous AI agents (e.g., LangChain, LangGraph, Vector DBs, OpenAI APIs).
- Experience with GitHub Actions, container orchestration, MCP Servers, and hybrid cloud deployment
- Familiarity with Dynatrace for production monitoring
- Experience supporting LangChain, LangGraph, and vector databases (e.g., Qdrant)
- Strong security, scripting, and CI/CD automation background
- Detail-oriented and proactive approach to system reliability, scalability, and security.
- Strong collaboration skills to efficiently support agile development cycles for agentic AI products.
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