Senior AIOps Engineer
Tasks
- Collaborate with cross functional teams for scalable secure and cost efficient AI operations
- Define and maintain SLOs and SLIs for AI systems
- Implement observability with monitoring logging alerting and dashboards
- Manage CI/CD pipelines and deployment automation
- Monitor detect and mitigate model drift and performance degradation
- Operate and improve AI infrastructure for model serving and LLM gateways and RAG pipelines
- Own reliability availability and performance of AI ML services in production
- Participate in incident management and post-incident reviews
- Support deployment workflows for ML models including canary blue green and A B testing
Perks/Benefits
- Accident insurance
- Fully remote
- Generous leave policies
- Health insurance
- Paid Holidays
- Retirement benefits
- Wellness programs
Skills/Tech-stack
Alerting | BigQuery | CI/CD | Cloud SQL | Cloud Storage | Cloud platform | Datadog | Distributed Systems | Docker | ELK Stack | GKE | Google Cloud | Google Cloud Platform | Google Cloud Storage | Grafana | Infrastructure as Code | Kubernetes | LLM Gateway | Logging | Machine Learning | Model Drift | Model Monitoring | Monitoring | Observability | Prometheus | PubSub | Python | RAG | Retrieval-Augmented Generation | SLIs | SLOs | Terraform | Vector Database | Vertex AI | “as-code”
Education
Roles
AIOps Engineer | DevOps | DevOps Engineer | Engineer | SRE | SRE Engineer
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