AI-Ops Engineer

Top 3 requirements to hire:

• 3+ years DevOps/SRE/Cloud Engineering
• Python + AWS infrastructure experience
• At least one AWS Associate level certification

Position Overview:

The AI-Ops Engineer is a key technical contributor responsible for evolving traditional DevOps into AI- Ops. 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 global online programs.

Key Responsibilities:

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.

Cloud Infrastructure & DevOps

• Design, build, and maintain scalable, secure AWS infrastructure using Infrastructure as Code (CloudFormation, 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.

Collaboration & Continuous Improvement

• 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.

Required Qualifications

Education & Certifications

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

Experience:

• 3+ years of experience in DevOps, 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.

Technical Skills:

• Languages: Python (required); Bash, Go, or TypeScript preferred.
• AIOps & Monitoring: CloudWatch, X-Ray, Prometheus, Grafana, Datadog, or Splunk with ML capabilities.
• Infrastructure as Code: AWS CloudFormation, Terraform, or AWS CDK.
• Containers & Orchestration: Docker, AWS ECS/Fargate, Kubernetes (EKS).
• AWS Services: Lambda, EC2, S3, API Gateway, EventBridge, CloudWatch, IAM, VPC, CodePipeline, SageMaker.
• CI/CD Tools: GitHub Actions, AWS CodePipeline, Jenkins, or GitLab CI.
• Data & Analytics: Experience with log aggregation, metrics analysis, and event correlation platforms.

Desired Attributes:

• Strong understanding of AIOps principles using AI to enhance, not just support, IT operations. Passion for automation and eliminating manual, repetitive operational tasks.
• Excellent problem-solving, debugging, and root cause analysis skills.
• Demonstrated ability to learn rapidly, adapt to new technologies, and continuously improve. Strong communication skills with ability to collaborate across technical and non-technical teams. Commitment to reliability, security, and operational excellence.
• Thrives in a fast-paced, evolving environment, proactively seeking opportunities to embed intelligence into systems and processes.

Success Metrics:

• Reduced mean time to detection (MTTD) and mean time to resolution (MTTR) through AI- driven automation.
• High system uptime and availability (99.9%+) for critical infrastructure.
• Decreased alert noise and improved signal-to-noise ratio in monitoring systems.
• Measurable cost savings through predictive optimization and automated remediation. Consistent adoption of AIOps best practices across the organization.

Working Conditions:

• Hybrid work model (2-3 days on campus).
• Collaborative, agile team culture with regular code reviews and paired development. Occasional on-call responsibilities for critical infrastructure.