Artificial Intelligence Infrastructure Specialist
3 days ago
We are seeking a highly skilled Artificial Intelligence Infrastructure Specialist to join our team at QpiAI. In this role, you will play a critical part in ensuring the high availability, scalability, and fault tolerance of AI workloads.
About the Role:
The ideal candidate will have a deep understanding of observability, automation, and the tools & practices needed to support AI workloads and build highly reliable, scalable systems. You will be responsible for managing Kubernetes environments, implementing observability pipelines, and ensuring the high availability, reliability, and performance of AI applications.
Key Responsibilities:
- Observability & Monitoring:
Design, implement, and maintain observability pipelines using tools like Prometheus, Loki, Elasticsearch, Fluentbit/Fluentd, Logstash, Filebeat/Metricbeat, Thanos, Vector.dev, Jaeger, Tempo, Grafana Agent, or Grafana Alloy. - Build effective Grafana/Kibana dashboards, set up metrics, logs, and trace collection, and create alerts for AI applications.
- Ensure observability across AI workloads, containers, and services to guarantee high availability and robust monitoring.
- Troubleshoot and resolve infrastructure and application issues, ensuring quick detection and resolution.
- Automation & Infrastructure as Code (IaC):
Automate infrastructure setups using Terraform, Ansible, Tekton, and Helm charts to ensure efficient, consistent, and repeatable deployments. - Support Kubernetes-based infrastructure, enabling auto-scaling, fault tolerance, and high availability for AI applications and microservices.
- Certain Technologies:
Implement CI/CD pipelines using tools such as Jenkins, Drone, GitLab CI, Tekton, ArgoCD, or FluxCD. - Build and maintain scalable, secure, and efficient pipelines for continuous integration, deployment, and monitoring.
- Collaborate with developers to improve the development lifecycle and reduce time-to-deployment.
- AIOps Focus:
Support AI frameworks like PyTorch, Flyte, Kubeflow, Ray, and KubeRay (with a strong focus on PyTorch and Flyte). - Optimize Kubernetes clusters for resource-efficient AI workload management, monitoring, and scaling.
- Collaborate with data scientists and engineers to deploy and monitor large-scale AI models in production environments.
Requirements:
Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
3+ years of experience in SRE, Platform Engineering, or DevOps roles, with a strong focus on Kubernetes and observability tools.
$120,000 - $160,000 per year.
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