DevOps Engineer
About The Position
The DevOps Engineer will own the deployment, scaling, and monitoring of generative AI systems across cloud-native and hybrid platforms. The role collaborates with AI engineers and infrastructure teams to ensure reliable and secure AI operations. The engineer develops and maintains CI/CD pipelines for autonomous agentic AI systems, builds and automates cloud infrastructure, and implements monitoring, logging, and alerting. The position also supports LLM applications on hybrid infrastructure, secures AI pipelines and sensitive data, configures observability platforms, automates event-driven workflows, and collaborates with QA on test automation integrations.
Requirements
- At least 4 years of experience in DevOps, MLOps, or infrastructure engineering.
- Extensive experience with automation and infrastructure management supporting AI agent workloads.
- Expertise with Docker, Kubernetes, Terraform, and orchestration tools for agentic AI platforms.
- Familiarity with platforms and frameworks used for autonomous AI agents, such as LangChain, LangGraph, vector databases, and 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 such as Qdrant.
- Strong security, scripting, and CI/CD automation background.
- Detail-oriented and proactive approach to system reliability, scalability, and security.
- Strong collaboration skills to support agile development cycles for agentic AI products.
Responsibilities
- Design and manage CI/CD pipelines using GitHub.
- Develop and maintain CI/CD pipelines tailored to deploying autonomous agentic AI systems and related software components.
- Build and automate cloud infrastructure optimized for deploying, scaling, and managing autonomous AI agents.
- Implement monitoring, logging, and alerting for agentic AI processes to support reliability, performance, and continuous improvement.
- Collaborate 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 applications on hybrid infrastructure, including public cloud and on-premises environments.
- Configure observability platforms such as Dynatrace for observability and incident response.
- Automate integrations and event-driven workflows using n8n.
- Secure AI pipelines, manage secrets, and enable role-based access controls.
- Collaborate with QA to integrate test automation tools such as Functionize, Mabl, and Port.
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What This Job Offers
Job Type
Full-time
Education Level
No Education Listed
