Hansen Technologies
DevOps Engineer
- Compensation
- Not disclosed
- Location
- Carlsbad, California, United States
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Full time
- Experience
- 4+ years
- Category
- Devops
- Posted
Skills
Must have
- 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.
Full description
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.