AI-Ops Engineer

AI‑Ops Engineer - ChatGPT Jobs

Location:

Stanford, CA (Hybrid, must be onsite 2–3 days on campus). Remote:
Yes. Hourly Pay: $60/hr employment Type:
Full-time.

Work Schedule:

40 hours/week, Monday─Friday 9 am‑6 pm. Duration: 12‑month assignment, possible extension or conversion.

• Implement AIOps solutions using ML algorithms for automation.
• Build anomaly detection systems.
• Create predictive maintenance workflows.
• Architect observability platforms and unified dashboards.
• Implement intelligent alerting systems using NLP and ML.
• Deploy APM solutions integrated with AI‑driven analytics.
• Design, build, and maintain scalable, secure AWS infrastructure using Infrastructure as Code.
• 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.
• Partner with cross‑functional teams.
• Use Git‑based version control and code review.
• Document operational procedures and AIOps workflows.
• Occasional on‑call responsibilities.

• 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.
• 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.
• Familiarity with ML/AI concepts.
• Languages:
  
  Python (required);
  Bash, Go, or Type Script preferred.
• AIOps & Monitoring:
  Cloud Watch, X‑Ray, Prometheus, Grafana, Datadog, or Splunk with ML capabilities.
• Infrastructure as Code: AWS Cloud Formation, Terraform, or AWS CDK.
• Containers & Orchestration:
  Docker, AWS ECS/Fargate, Kubernetes (EKS).
• AWS Services:
  Lambda, EC2, S3, API Gateway, Event Bridge, Cloud Watch, IAM, VPC, Code Pipeline, Sage Maker.
• CI/CD Tools:
  Git Hub Actions, AWS Code Pipeline, Jenkins, or Git Lab CI.
• Data & Analytics:
  Experience with log aggregation, metrics analysis, and event correlation platforms.
• Strong understanding of AIOps principles.
• Passion for automation.
• Excellent problem‑solving, debugging, and root cause analysis skills.
• Ability to learn rapidly and adapt to new technologies.
• Strong communication skills.
• Commitment to reliability, security, and operational excellence.
• Thrives in a fast‑paced, evolving environment.