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AI Infrastructure Engineer​/MLOps

Job in San Francisco, San Francisco County, California, 94199, USA
Listing for: EITACIES Inc.
Full Time position
Listed on 2026-06-17
Job specializations:
  • IT/Tech
    SRE/Site Reliability, AWS, AI Engineer (Applied/Software), Cloud Computing: Infrastructure & Operations
Salary/Wage Range or Industry Benchmark: 120000 - 150000 USD Yearly USD 120000.00 150000.00 YEAR
Job Description & How to Apply Below
Position: AI Infrastructure Engineer / MLOps

Benefits

  • 401(k)
AI Infrastructure Engineer / MLOps

San Francisco Bay Area, CA (100% Onsite)

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
Required Skills
  • 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 Dev Ops best practices

    Experience with monitoring, logging, and observability platforms
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