AI-Ops Engineer

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Direct message the job poster from ALOIS Solutions

Location: Stanford, CA

• The AI-Ops Engineer is a key technical contributor responsible for evolving traditional Dev Ops into AI-Ops at CGOE.
• This role leverages AI and machine learning to automate and enhance IT operations - including performance monitoring, anomaly detection, root cause analysis, and automated remediation.
• Working at the intersection of cloud infrastructure, AI-driven automation, and operational excellence, the engineer embeds intelligence into infrastructure, deployment, and monitoring to ensure high availability, predictive issue resolution, and operational efficiency across CGOE's global online programs.

• AI-Driven Operations & Automation:
• Implement AIOps solutions that use ML algorithms to automate performance monitoring, workload scheduling, and infrastructure management.
• Build anomaly detection systems that identify infrastructure issues before they impact users.
• Develop automated root cause analysis capabilities using ML to correlate events and filter noise from critical alerts.
• Create predictive maintenance workflows that analyze historical patterns to proactively mitigate issues.
• Design and implement automated remediation scripts that respond to incidents without human intervention.
• Observability & Intelligent Monitoring:
• Architect comprehensive observability platforms that aggregate data from disparate sources into unified dashboards.
• Implement intelligent alerting systems using NLP and ML to reduce alert fatigue and surface actionable insights.
• Build real-time analytics dashboards for coordinated diagnosis across teams.
• Deploy application performance monitoring (APM) solutions integrated with AI-driven analytics. Ensure end-to-end visibility across cloud infrastructure, applications, and AI/ML workloads.
• Design, build, and maintain scalable, secure AWS infrastructure using Infrastructure as Code (Cloud Formation, Terraform, or CDK).
• Implement and manage containerized environments using Docker, AWS ECS, Fargate, and Kubernetes (EKS).
• Build CI/CD pipelines for continuous delivery, integrating AI-powered code quality and deployment optimization.
• Manage cloud automation and optimization to improve cost-efficiency and resource utilization.
• Ensure compliance with Stanford and regulatory standards (FERPA, GDPR) for secure data handling and governance.
• Partner with cross-functional teams to implement domain-agnostic AIOps solutions across the organization.
• Use Git-based version control and code review best practices as part of a collaborative, agile workflow.
• Document operational procedures, runbooks, and AIOps workflows for team knowledge sharing.
• Continuously evaluate and adopt emerging AIOps tools, AWS services, and AI-driven automation technologies.
• Contribute to building an AI-first operational culture that prioritizes automation and predictive capabilities.

• Bachelor's degree in Computer Science, Dev Ops, Cloud Engineering, or a related field (Master's preferred).
• AWS certification preferred (Solutions Architect, Sys Ops Administrator, or Dev Ops Engineer);
  Professional-level certification a plus.

• 3+ years of experience in Dev Ops, SRE, or Cloud Engineering roles.
• 2+ years of hands-on experience with AWS infrastructure (EC2, ECS, Lambda, S3, IAM, VPC).
• Experience implementing monitoring, observability, and alerting solutions at scale.
• Familiarity with ML/AI concepts and their application to operational automation.