AI Infrastructure Engineer / MLOps

EITAcies, Inc.
San Francisco, CA, United States
4 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Amazon Web Services Linux DevOps Distributed Systems Python (Programming Language) Linux System Administration Machine Learning Performance Tuning Ansible AI Infrastructure Data Logging
+9 more
Cloud Platform System Large Language Models Multi-Agent Systems Software Troubleshooting Kubernetes Infrastructure Automation Frameworks Machine Learning Operations Virtual Agents AWS EKS

Job 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

Requirements

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

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