Infrastructure Engineer

Job summary

Leads the design, deployment, and production operation of infrastructure platforms for Generative AI and Predictive AI workloads, including model training, inference, and batch processing. Builds and operates GPU-enabled OpenShift and Kubernetes platforms, including large multi-node SuperPod clusters, and establishes observability, SLIs/SLOs, and actionable alerts using Grafana, Splunk, and telemetry tools. Develops AIOps automation and leads incident analysis to improve reliability in regulated enterprise environments.

Benefits

Qualifications

• At least 5 years of infrastructure engineering experience.
• At least 5 years troubleshooting complex end-to-end architectures, including CI/CD pipelines.
• At least 5 years of Linux systems experience.
• At least 4 years supporting AI/ML platforms.
• At least 4 years of production Kubernetes or container platform experience.
• Experience with Generative AI and Predictive AI platforms.
• Hands-on GPU platform operations, including scheduling, quota management, and performance tuning.
• Experience with OpenShift in GPU-enabled, multi-tenant environments.
• Experience designing or operating GPU SuperPods.
• Experience with Grafana, Splunk, and custom telemetry pipelines.
• Experience building AI- or agent-driven AIOps automation.
• Experience supporting AI/ML workloads on GCP and Azure, including GPU-backed services and managed AI infrastructure.
• Experience operating hybrid or multi-cloud AI platforms.
• Understanding of cloud-native services, networking, identity, and cost optimization for AI platforms.
• Experience monitoring AI signals such as inference latency and GPU utilization.
• Experience with business continuity, disaster recovery, resiliency, and highly available architectures.

Responsibilities

• Lead infrastructure initiatives for Generative AI and Predictive AI platforms from design through production operations.
• Provide technical leadership for platforms supporting AI/ML training, inference, and batch workloads.
• Design, deploy, and operate GPU-optimized OpenShift container platforms.
• Build and operate scalable GPU SuperPod architectures and large multi-node GPU clusters.
• Manage monitoring, alerting, and observability using Grafana, Splunk, and enterprise telemetry tools.
• Define SLIs and SLOs and create alerts for performance, capacity, and resiliency risks.
• Build AI- and agent-based automation for self-healing, scaling, diagnostics, and incident remediation.
• Apply AIOps to reduce alert fatigue and improve platform reliability.
• Lead production incident analysis and implement root-cause prevention.
• Mentor engineers and influence distributed stakeholders.

Skills

Work schedule

Industry

Relocation