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AI Infrastructure Engineer / MLOps
Eitacies Inc
- Location
- Onsite (San Francisco, CA)
- Compensation
- $50 - $60/hr
- Employment
- Full-time
- Level
- Senior Level
Posted 2 weeks ago
About the Role
EITACIES is seeking an experienced AI Infrastructure Engineer to build and maintain large-scale AI and machine learning platforms in cloud-native environments. This role involves designing, deploying, and supporting scalable infrastructure, managing Kubernetes, and implementing MLOps workflows.
Skills
Linux Systems Administration
Python
Kubernetes
AWS EKS
Ansible
MLOps
Infrastructure Automation
Distributed Systems
Performance Optimization
Observability
Agentic AI
AI Orchestration
LLM Applications
Infrastructure-as-Code
DevOps
Cloud Native Environments
Benefits
- 401(k)
Full job details
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 DevOps best practices
- Experience with monitoring, logging, and observability platforms