Senior AI Infrastructure Engineer

Lead complex infrastructure initiatives supporting Generative AI and Predictive AI platforms from design to production operations.

• Serve as a technical lead for platforms supporting AI/ML model training, inference, and batch workloads.

• Design, build, deploy, and operate OpenShift-based container platforms optimized for high-performance GPU workloads.

• Build, support and operate scalable GPU SuperPod architecture with large multi-node GPU clusters.

• Own monitoring, alerting, and observability using Grafana, Splunk, and enterprise telemetry tools.

• Define SLIs/SLOs and build actionable alerts to proactively detect performance, capacity, and resiliency risks.

• Build AI- and agent-based automation tools for self-healing, scaling, diagnostics, and incident remediation.

• Apply AIOps techniques to reduce alert fatigue and improve platform reliability.

• Lead production incident analysis and ensure operational rigor and root-cause prevention.

• Mentor engineers and influence stakeholders across a geographically distributed organization.

5 years of infrastructure engineering experience.

• 5 years troubleshooting complex end-to-end architectures(including CI/CD pipeline).

• 5 years Linux systems experience.

• 4 years supporting AI/ML platforms.

• 4 years of Kubernetes / container platform experience including production support.

Desired Qualifications

• Experience with Generative AI and Predictive AI platforms.

• Hands-on GPU platform operations including scheduling, quota, and performance tuning.

• Experience with OpenShift in GPU-enabled, multi-tenant environments.

• Experience designing or operating GPU Super Pods.

• Deep experience with observability using Grafana, Splunk, and custom telemetry pipelines.

• Experience building AI- or agent-driven automation tooling (AIOps).

• Hands-on 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, with an understanding of cloud-native services, networking, identity, and cost optimization for Generative and Predictive AI

• Strong monitoring of AI signals such as inference latency and GPU utilization.

• Experience with BCP/DR, resiliency, and highly available architectures.