AI-Ops Engineer

Avispa Technology•Stanford, CA

2d•$60 - $60•Hybrid

About The Position

A leading university seeks an AI-Ops Engineer . The successful candidate will be responsible for evolving traditional DevOps into AI- Ops at the Engineering Center. This role leverages AI and machine learning to automate and enhance IT operations. The company offers a family-oriented culture and environment!

Requirements

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.
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.
Familiarity with ML/AI concepts and their application to operational automation.
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.
Strong understanding of AIOps principles—using AI to enhance, not just support, IT operations is preferred.

Nice To Haves

Passion for automation and eliminating manual, repetitive operational tasks is preferred.
Excellent problem-solving, debugging, and root cause analysis skills are preferred.
Demonstrated ability to learn rapidly, adapt to new technologies, and continuously improve is preferred.
Strong communication skills withthe ability to collaborate across technical and non-technical teams are preferred.
Commitment to reliability, security, and operational excellence is preferred.
Thrives in a fast-paced, evolving environment, proactively seeking opportunities to embed intelligence into systems and processes is preferred.

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.
Create predictive maintenance workflows that analyze historical patterns to proactively mitigate issues.
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.
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.
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.
Occasional on-call responsibilities for critical infrastructure.