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# AI Infrastructure Engineer / MLOps

- **Company:** EITAcies, Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Linux, DevOps, Distributed Systems, Python (Programming Language), Linux System Administration, Machine Learning, Performance Tuning, Ansible, AI Infrastructure, Data Logging, Cloud Platform System, Large Language Models, Multi-Agent Systems, Software Troubleshooting, Kubernetes, Infrastructure Automation Frameworks, Machine Learning Operations, Virtual Agents, AWS EKS
- **Published:** August 9, 2026
- **Apply:** https://www.wayup.com/i-j-AI-Infrastructure-Engineer-MLOps-Eitacies-Inc-503414544701534/

## About the Role

10+ years of Linux systems administration and engineering experience
10+ years of Python development and automation experience
5+ years of Kubernetes administration and operations
5+ years of AWS cloud experience, including AWS EKS
5+ years of infrastructure automation using Ansible
Experience supporting production-scale distributed systems
Strong troubleshooting and performance optimization skills
Experience working in enterprise environments
Preferred Skills
Experience with MLOps platforms and machine learning infrastructure
Exposure to Agentic AI architectures and AI orchestration frameworks
Experience supporting LLM-based applications and AI workloads
Knowledge of infrastructure-as-code and DevOps best practices
Experience with monitoring, logging, and observability platforms

## Description

EITACIES is looking for an experienced AI Infrastructure Engineer to support large scale AI and machine learning platforms running in cloud native environments.
Responsibilities
Design, deploy, and support scalable AI/ML infrastructure platforms
Manage Kubernetes environments running in AWS EKS
Build and maintain infrastructure automation using Python and Ansible
Support MLOps workflows including model deployment, monitoring, and operationalization
Troubleshoot complex Linux-based production environments
Partner with engineering teams to improve platform reliability, scalability, and performance
Implement observability, monitoring, and operational best practices across AI systems
Support modern AI architectures and agent-based workflows


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